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Almousa SA. Quality of primary total knee arthroplasty operative reports in a tertiary teaching hospital. World J Orthop 2025; 16:104438. [DOI: 10.5312/wjo.v16.i5.104438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2024] [Revised: 03/22/2025] [Accepted: 04/14/2025] [Indexed: 05/16/2025] Open
Abstract
BACKGROUND Operative reports (OP-Rs) are essential for communication among healthcare providers. They require accuracy and completeness to serve as a quality indicator of patient care. Objective assessment of primary total knee replacement (TKR) OP-Rs has never been reported. Therefore, a standardized benchmark for assessment and factors affecting the completeness of TKR OP-Rs needs to be evaluated.
AIM To evaluate the completeness rate of primary TKR OP-Rs in a teaching hospital and to assess the factors affecting completeness.
METHODS A retrospective review of 58 consecutive primary TKR OP-Rs in a tertiary teaching hospital were included in this study. We used document analysis to review the OP-Rs against a standardized list of six subsets of mandatory variables. The correlation between the percentage of completeness and the specific variables was determined.
RESULTS After analyzing 58 cases, we found that the time to documentation was 1.5 hours. Out of the 52 mandatory variables, a median of 30 variables were documented yielding a completeness of 58%. Administrative, procedural, exposure, and implant variables were documented the most often, whereas clinical and process variables were most frequently left uncompleted. The documentation of the operative maneuver was variable. There was no association between the completeness of the reports and the time to documentation, documenter level, complication rate, operative duration, or length of hospital stay.
CONCLUSION Multiple variables were left undocumented on the unstructured primary TKR OP-Rs. The completeness percentage will likely improve after the implementation of a standardized structured OP-R.
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Affiliation(s)
- Sulaiman A Almousa
- Department of Orthopedic Surgery, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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2
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Rudolph CN, Wakili P, Rickmann A, Januschowski K, McCrae P, Foth K, Petris M, Schilk A, Kiefer S, Rohm K, Ahamed S, Weiler G, Stennes M, Trouvain A, Szurman P, Boden KT. [Implementation and evaluation of a solution for automated documentation of the doctor-patient dialogue (ADAPI) in ophthalmology using the example of the IVI routine]. DIE OPHTHALMOLOGIE 2025; 122:210-217. [PMID: 39812666 DOI: 10.1007/s00347-024-02165-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 11/24/2024] [Accepted: 12/03/2024] [Indexed: 01/16/2025]
Abstract
BACKGROUND The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide a solution. OBJECTIVE The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization. METHODS In a prospective monocentric study at the Sulzbach Eye Clinic, 50 patients were included in the period from 2020 to 2022. As part of a project funded by the "Zentrale Initiative des Mittelstandes" (ZIM), a demonstrator was developed with the consortium partners and integrated into the hospital information system (HIS). For qualitative and quantitative evaluation, a survey of patients was carried out using a questionnaire before and after implementation of the ADAPI module, supplemented by a determination of acceptance and possible time saving for users. RESULTS The ADAPI module was successfully integrated into the HIS. The documentation of the findings and connection of the subsequent processes could be automated. An improvement in the DPC was reported for 13/50 patients (26%). The majority 35/50 (70%) did not notice any subjective change. The average duration of the conversation in the area of conventional documentation was 4.46 min (1-10 min) and 3.9 min with ADAPI (1-10 min). CONCLUSION With respect to the quality of the conversation within the DPC, no relevant differences could be shown with both types of documentation; however, larger case numbers and other areas of application still need to be evaluated. In the long term, the use of automated documentation solutions will sustainably improve the efficiency, completeness and consistency of clinical documentation.
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Affiliation(s)
- Clemens N Rudolph
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland.
| | - Philip Wakili
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland
| | - Annekatrin Rickmann
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland
| | - Kai Januschowski
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland
| | | | | | | | | | - Stephan Kiefer
- Fraunhofer Institut für Biomedizinische Technik (IBMT), Sulzbach/Saar, Deutschland
| | - Kerstin Rohm
- Fraunhofer Institut für Biomedizinische Technik (IBMT), Sulzbach/Saar, Deutschland
| | - Suhail Ahamed
- Fraunhofer Institut für Biomedizinische Technik (IBMT), Sulzbach/Saar, Deutschland
| | - Gabriele Weiler
- Fraunhofer Institut für Biomedizinische Technik (IBMT), Sulzbach/Saar, Deutschland
| | - Matthias Stennes
- Fraunhofer Institut für Digitale Medientechnologie (IDMT), Oldenburg, Deutschland
| | - André Trouvain
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland
| | - Peter Szurman
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland
- Klaus Heimann Eye Research Institute (KHERI), Sulzbach/Saar, Deutschland
| | - Karl T Boden
- Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland
- Klaus Heimann Eye Research Institute (KHERI), Sulzbach/Saar, Deutschland
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3
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Fogleman BM, Goldman M, Holland AB, Dyess G, Patel A. Charting Tomorrow's Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future. Cureus 2024; 16:e58032. [PMID: 38738104 PMCID: PMC11088287 DOI: 10.7759/cureus.58032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2024] [Indexed: 05/14/2024] Open
Abstract
Electronic health record (EHR) systems have developed over time in parallel with general advancements in mainstream technology. As artificially intelligent (AI) systems rapidly impact multiple societal sectors, it has become apparent that medicine is not immune from the influences of this powerful technology. Particularly appealing is how AI may aid in improving healthcare efficiency with note-writing automation. This literature review explores the current state of EHR technologies in healthcare, specifically focusing on possibilities for addressing EHR challenges through the automation of dictation and note-writing processes with AI integration. This review offers a broad understanding of existing capabilities and potential advancements, emphasizing innovations such as voice-to-text dictation, wearable devices, and AI-assisted procedure note dictation. The primary objective is to provide researchers with valuable insights, enabling them to generate new technologies and advancements within the healthcare landscape. By exploring the benefits, challenges, and future of AI integration, this review encourages the development of innovative solutions, with the goal of enhancing patient care and healthcare delivery efficiency.
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Affiliation(s)
- Brody M Fogleman
- Internal Medicine, Edward Via College of Osteopathic Medicine - Carolinas, Spartanburg, USA
| | - Matthew Goldman
- Neurological Surgery, Houston Methodist Hospital, Houston, USA
| | - Alexander B Holland
- General Surgery, Edward Via College of Osteopathic Medicine - Carolinas, Spartanburg, USA
| | - Garrett Dyess
- Medicine, University of South Alabama College of Medicine, Mobile, USA
| | - Aashay Patel
- Neurological Surgery, University of Florida College of Medicine, Gainesville, USA
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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: 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: 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.
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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
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5
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Falcetta FS, de Almeida FK, Lemos JCS, Goldim JR, da Costa CA. Automatic documentation of professional health interactions: A systematic review. Artif Intell Med 2023; 137:102487. [PMID: 36868684 DOI: 10.1016/j.artmed.2023.102487] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 01/04/2023] [Accepted: 01/04/2023] [Indexed: 01/20/2023]
Abstract
Electronic systems are increasingly present in the healthcare system and are often related to improved medical care. However, the widespread use of these technologies ended up building a relationship of dependence that can disrupt the doctor-patient relationship. In this context, digital scribes are automated clinical documentation systems that capture the physician-patient conversation and then generate the documentation for the appointment, enabling the physician to engage with the patient entirely. We have performed a systematic literature review on intelligent solutions for automatic speech recognition (ASR) with automatic documentation during a medical interview. The scope included only original research on systems that could detect speech and transcribe it in a natural and structured fashion simultaneously with the doctor-patient interaction, excluding speech-to-text-only technologies. The search resulted in a total of 1995 titles, with eight articles remaining after filtering for the inclusion and exclusion criteria. The intelligent models mainly consisted of an ASR system with natural language processing capability, a medical lexicon, and structured text output. None of the articles had a commercially available product at the time of the publication and reported limited real-life experience. So far, none of the applications has been prospectively validated and tested in large-scale clinical studies. Nonetheless, these first reports suggest that automatic speech recognition may be a valuable tool in the future to facilitate medical registration in a faster and more reliable manner. Improving transparency, accuracy, and empathy could drastically change how patients and doctors experience a medical visit. Unfortunately, clinical data on the usability and benefits of such applications is almost non-existent. We believe that future work in this area is necessary and needed.
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Affiliation(s)
- Frederico Soares Falcetta
- Software Innovation Laboratory - Softwarelab, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-750, RS, Brazil; Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-903, RS, Brazil.
| | | | - Janaína Conceição Sutil Lemos
- Software Innovation Laboratory - Softwarelab, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-750, RS, Brazil.
| | - José Roberto Goldim
- Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-903, RS, Brazil.
| | - Cristiano André da Costa
- Software Innovation Laboratory - Softwarelab, Universidade do Vale do Rio dos Sinos - Unisinos, Av. Unisinos 950, São Leopoldo, 93022-750, RS, Brazil.
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Ye JJ, Tan MR, Shum CH. Using Timestamp Data to Assess the Impact of Voice Recognition on the Efficiency of Grossing Biopsies. Arch Pathol Lab Med 2021; 145:599-606. [PMID: 32960950 DOI: 10.5858/arpa.2020-0115-oa] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Studies on the adoption of voice recognition in health care have mostly focused on turnaround time and error rate, with less attention paid to the impact on the efficiency of the providers. OBJECTIVE.— To study the impact of voice recognition on the efficiency of grossing biopsy specimens. DESIGN.— Timestamps corresponding to barcode scanning for biopsy specimen bottles and cassettes were retrieved from the pathology information system database. The time elapsed between scanning a specimen bottle and the corresponding first cassette was the length of time spent on the gross processing of that specimen and is designated as the specimen time. For the first specimen of a case, the specimen time additionally included the time spent on dictating the clinical information. Therefore, the specimen times were divided into the following 2 categories: first-specimen time and subsequent-specimen time. The impact of voice recognition on specimen times was studied using both univariate and multivariate analyses. RESULTS.— Specimen complexity, prosector variability, length of clinical information text, and the number of biopsies the prosector grossed that day were the major determinants of specimen times. Adopting voice recognition had a negligible impact on specimen times. CONCLUSIONS.— Adopting voice recognition in the gross room removes the need to hire transcriptionists without negatively impacting the efficiency of the prosectors, resulting in an overall cost saving. Using computer scripting to automatically enter clinical information (received through the electronic order interface) into report templates may potentially increase the grossing efficiency in the future.
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Affiliation(s)
- Jay J Ye
- From Dahl-Chase Pathology Associates, Bangor, Maine
| | | | - Chung H Shum
- From Dahl-Chase Pathology Associates, Bangor, Maine
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Alcaraz-Mateos E, Turic I, Nieto-Olivares A, Pérez-Ramos M, Poblet E. Head-tracking as an interface device for image control in digital pathology: a comparative study. REVISTA ESPANOLA DE PATOLOGIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ANATOMIA PATOLOGICA Y DE LA SOCIEDAD ESPANOLA DE CITOLOGIA 2020; 53:213-217. [PMID: 33012490 PMCID: PMC7343653 DOI: 10.1016/j.patol.2020.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 05/26/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND Inasmuch as the conventional mouse is not an ideal input device for digital pathology, the aim of this study was to evaluate alternative systems with the goal of identifying a natural user interface (NUI) for controlling whole slide images (WSI). DESIGN Four pathologists evaluated three webcam-based, head-tracking mouse emulators: Enable Viacam (eViacam, CREA Software), Nouse (JLG Health Solutions Inc), and Camera Mouse (CM Solutions Inc). Twenty WSI dermatopathological cases were randomly selected and examined with Image Viewer (Ventana, AZ, USA). The NASA-TLX was used to rate the perceived workload of using these systems and time was recorded. In addition, a satisfaction survey was used. RESULTS The mean total time needed for diagnosis with Camera Mouse, eViacam, and Nouse was 18'57", 19'37" and 22'32", respectively (57/59/68seconds per case, respectively). The NASA-TLX workload score, where lower scores are better, was 42.1 for eViacam, 53.3 for Nouse and 60.62 for Camera Mouse. This correlated with the pathologists' degree of satisfaction on a scale of 1-5: 3.4 for eViacam, 3 for Nouse, and 2 for Camera Mouse (p<0.05). CONCLUSIONS Head-tracking systems enable pathologists to control the computer cursor and virtual slides without their hands using only a webcam as an input device. - Of the three software solutions examined, eViacam seems to be the best of those evaluated in this study, followed by Nouse and, finally, Camera Mouse. - Further studies integrating other systems should be performed in conjunction with software developments to identify the ideal device for digital pathology.
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Affiliation(s)
- Eduardo Alcaraz-Mateos
- Servicio de Anatomía Patológica. Hospital Universitario Morales Meseguer, Murcia, España. Av. Marqués de los Vélez s/n, 30008, Murcia, España.
| | - Iva Turic
- Faculty of Medicine, University of Split, Split, Croatia. Poljička cesta 35, 21000, Split, Croacia
| | - Andrés Nieto-Olivares
- Servicio de Anatomía Patológica. Hospital Universitario Morales Meseguer, Murcia, España. Av. Marqués de los Vélez s/n, 30008, Murcia, España
| | - Miguel Pérez-Ramos
- Servicio de Anatomía Patológica. Hospital Universitario Morales Meseguer, Murcia, España. Av. Marqués de los Vélez s/n, 30008, Murcia, España
| | - Enrique Poblet
- Servicio de Anatomía Patológica. Hospital Universitario Reina Sofía, Murcia, España. Av. Intendente Jorge Palacios, 1, 30003, Murcia, España
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8
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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: 28] [Impact Index Per Article: 4.7] [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]
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9
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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: 5.8] [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.
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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
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Gao ZH, Zorychta E, Karamchandani J, Michel RP, Brimo F, Telleria C, Camilleri-Broët S, Auger M, Nguyen VH, Spatz A. Revitalising an academic pathology department: lessons learnt. J Clin Pathol 2018; 72:213-220. [PMID: 30467243 DOI: 10.1136/jclinpath-2018-205516] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 10/28/2018] [Indexed: 11/03/2022]
Abstract
Pathology is a specialty that bridges basic medical science and clinical practice. In the era of personalised medicine, this specialty is facing unprecedented challenges. Some of these challenges are institution-specific, while many are shared worldwide at different magnitude. This review shares our team efforts in the past 5 years, 2012-2017, to revitalise a century-old academic pathology department in three aspects: administration, clinical service and academic development. The lessons learnt and insights gained from our experience may provide guidance to leaders in pathology or in other related specialties.
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Affiliation(s)
- Zu-Hua Gao
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - Edith Zorychta
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - Jason Karamchandani
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - René P Michel
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | | | - Carlos Telleria
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - Sophie Camilleri-Broët
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - Manon Auger
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - Van-Hung Nguyen
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
| | - Alan Spatz
- Department of Pathology, McGill University and MGill University Health Center, Montreal, Quebec, Canada
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11
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Gao ZH. Chairing an academic pathology department: challenges and opportunities. J Clin Pathol 2018; 72:206-212. [PMID: 29705737 DOI: 10.1136/jclinpath-2017-204963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 02/08/2018] [Accepted: 04/10/2018] [Indexed: 11/04/2022]
Abstract
Understanding the heterogeneity of departmental structure, service model and job descriptions for different pathology chairs, this review highlights some common challenges and opportunities facing most pathology chairs in academic institutions. The review is divided into three sections: clinical service, academic development and administration. The views and insights from this review may provide guidance to new chairs and emerging leaders in pathology and other relevant specialties.
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Affiliation(s)
- Zu-Hua Gao
- Department of Pathology, McGill University, Montreal, Quebec, Canada
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12
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Gu Y, Li X, Chen S, Li H, Farneth RA, Marsic I, Burd RS. Language-Based Process Phase Detection in the Trauma Resuscitation. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS. IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS 2017; 2017:239-247. [PMID: 30357019 PMCID: PMC6196035 DOI: 10.1109/ichi.2017.50] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Process phase detection has been widely used in surgical process modeling (SPM) to track process progression. These studies mostly used video and embedded sensor data, but spoken language also provides rich semantic information directly related to process progression. We present a long-short term memory (LSTM) deep learning model to predict trauma resuscitation phases using verbal communication logs. We first use an LSTM to extract the sentence meaning representations, and then sequentially feed them into another LSTM to extract the meaning of a sentence group within a time window. This information is ultimately used for phase prediction. We used 24 manually-transcribed trauma resuscitation cases to train, and the remaining 6 cases to test our model. We achieved 79.12% accuracy, and showed performance advantages over existing visual-audio systems for critical phases of the process. In addition to language information, we evaluated a multimodal phase prediction structure that also uses audio input. We finally identified the challenges of substituting manual transcription with automatic speech recognition in trauma resuscitation.
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Affiliation(s)
- Yue Gu
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Xinyu Li
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Shuhong Chen
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Hunagcan Li
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Richard A Farneth
- Trauma and Burn Surgery, Children's National Medical Center, Washington, DC, USA
| | - Ivan Marsic
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
| | - Randall S Burd
- Trauma and Burn Surgery, Children's National Medical Center, Washington, DC, USA
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13
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Hartman DJ. Enhancing and Customizing Laboratory Information Systems to Improve/Enhance Pathologist Workflow. Clin Lab Med 2016; 36:31-9. [PMID: 26851662 DOI: 10.1016/j.cll.2015.09.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Optimizing pathologist workflow can be difficult because it is affected by many variables. Surgical pathologists must complete many tasks that culminate in a final pathology report. Several software systems can be used to enhance/improve pathologist workflow. These include voice recognition software, pre-sign-out quality assurance, image utilization, and computerized provider order entry. Recent changes in the diagnostic coding and the more prominent role of centralized electronic health records represent potential areas for increased ways to enhance/improve the workflow for surgical pathologists. Additional unforeseen changes to the pathologist workflow may accompany the introduction of whole-slide imaging technology to the routine diagnostic work.
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Affiliation(s)
- Douglas J Hartman
- Department of Anatomic Pathology, University of Pittsburgh Medical Center, 200 Lothrop Street, A-607, Pittsburgh, PA 15213, USA.
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Abstract
Laboratory information systems (LISs) supply mission-critical capabilities for the vast array of information-processing needs of modern laboratories. LIS architectures include mainframe, client-server, and thin client configurations. The LIS database software manages a laboratory's data. LIS dictionaries are database tables that a laboratory uses to tailor an LIS to the unique needs of that laboratory. Anatomic pathology LIS (APLIS) functions play key roles throughout the pathology workflow, and laboratories rely on LIS management reports to monitor operations. This article describes the structure and functions of APLISs, with emphasis on their roles in laboratory operations and their relevance to pathologists.
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Affiliation(s)
- Walter H Henricks
- Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, L21, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
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Chan SW, Liew LH, Wong GR, Kallarakkal TG, Abraham MT, Ramanathan A, Zain RB. Audit of Turnaround Time for a Training Oral Histopathology Laboratory in Malaysia. Int J Surg Pathol 2016; 24:401-9. [PMID: 27006298 DOI: 10.1177/1066896916639372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Turnaround time (TAT) is the benchmark to assess the performance of a laboratory, pathologists, and pathology services, but there are few articles on TAT of surgical pathology, particularly in relation to oral or head and neck specimens. This study investigates the TAT for oral histopathology reporting in an academic institution's training laboratory and offers recommendations to achieve better overall quality of diagnostic services. METHODS This study examined data obtained from biopsy request forms for specimens received from the Oro-Maxillofacial Surgery Department of Hospital Tengku Ampuan Rahimah Klang in the Oral Pathology Diagnostic Laboratory of the Faculty of Dentistry, University of Malaya, over a period of 3 years between January 2012 and October 2014. RESULTS TAT for surgical and decalcified specimens were increased significantly compared to biopsies. Additional special handling did not influence TAT, but increased specimen volume resulted in greater TAT. Slide interpretation was the most time-consuming stage during histopathology reporting. Overall, mean TAT was acceptable for most specimens, but the TAT goals were less than satisfactory. CONCLUSION A TAT goal appropriate for this laboratory may hence be established based on this study. Collective efforts to improve the TAT for various specimens are essential for better laboratory performance in the future.
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Ye JJ. Artificial Intelligence for Pathologists Is Not Near--It Is Here: Description of a Prototype That Can Transform How We Practice Pathology Tomorrow. Arch Pathol Lab Med 2015; 139:929-35. [PMID: 26125433 DOI: 10.5858/arpa.2014-0478-oa] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
CONTEXT Pathologists' daily tasks consist of both the professional interpretation of slides and the secretarial tasks of translating these interpretations into final pathology reports, the latter of which is a time-consuming endeavor for most pathologists. OBJECTIVE To describe an artificial intelligence that performs secretarial tasks, designated as Secretary-Mimicking Artificial Intelligence (SMILE). DESIGN The underling implementation of SMILE is a collection of computer programs that work in concert to "listen to" the voice commands and to "watch for" the changes of windows caused by slide bar code scanning; SMILE responds to these inputs by acting upon PowerPath Client windows (Sunquest Information Systems, Tucson, Arizona) and its Microsoft Word (Microsoft, Redmond, Washington) Add-In window, eventuating in the reports being typed and finalized. Secretary-Mimicking Artificial Intelligence also communicates relevant information to the pathologist via the computer speakers and message box on the screen. RESULTS Secretary-Mimicking Artificial Intelligence performs many secretarial tasks intelligently and semiautonomously, with rapidity and consistency, thus enabling pathologists to focus on slide interpretation, which results in a marked increase in productivity, decrease in errors, and reduction of stress in daily practice. Secretary-Mimicking Artificial Intelligence undergoes encounter-based learning continually, resulting in a continuous improvement in its knowledge-based intelligence. CONCLUSIONS Artificial intelligence for pathologists is both feasible and powerful. The future widespread use of artificial intelligence in our profession is certainly going to transform how we practice pathology.
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Affiliation(s)
- Jay J Ye
- From Dahl-Chase Pathology Associates, Bangor, Maine
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Suominen H, Zhou L, Hanlen L, Ferraro G. Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations. JMIR Med Inform 2015; 3:e19. [PMID: 25917752 PMCID: PMC4427705 DOI: 10.2196/medinform.4321] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Accepted: 03/07/2015] [Indexed: 12/16/2022] Open
Abstract
Background Over a tenth of preventable adverse events in health care are caused by failures in information flow. These failures are tangible in clinical handover; regardless of good verbal handover, from two-thirds to all of this information is lost after 3-5 shifts if notes are taken by hand, or not at all. Speech recognition and information extraction provide a way to fill out a handover form for clinical proofing and sign-off. Objective The objective of the study was to provide a recorded spoken handover, annotated verbatim transcriptions, and evaluations to support research in spoken and written natural language processing for filling out a clinical handover form. This dataset is based on synthetic patient profiles, thereby avoiding ethical and legal restrictions, while maintaining efficacy for research in speech-to-text conversion and information extraction, based on realistic clinical scenarios. We also introduce a Web app to demonstrate the system design and workflow. Methods We experiment with Dragon Medical 11.0 for speech recognition and CRF++ for information extraction. To compute features for information extraction, we also apply CoreNLP, MetaMap, and Ontoserver. Our evaluation uses cross-validation techniques to measure processing correctness. Results The data provided were a simulation of nursing handover, as recorded using a mobile device, built from simulated patient records and handover scripts, spoken by an Australian registered nurse. Speech recognition recognized 5276 of 7277 words in our 100 test documents correctly. We considered 50 mutually exclusive categories in information extraction and achieved the F1 (ie, the harmonic mean of Precision and Recall) of 0.86 in the category for irrelevant text and the macro-averaged F1 of 0.70 over the remaining 35 nonempty categories of the form in our 101 test documents. Conclusions The significance of this study hinges on opening our data, together with the related performance benchmarks and some processing software, to the research and development community for studying clinical documentation and language-processing. The data are used in the CLEFeHealth 2015 evaluation laboratory for a shared task on speech recognition.
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Affiliation(s)
- Hanna Suominen
- Canberra Research Laboratory, Machine Learning Research Group, NICTA, Canberra, ACT, Australia.
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Suominen H, Johnson M, Zhou L, Sanchez P, Sirel R, Basilakis J, Hanlen L, Estival D, Dawson L, Kelly B. Capturing patient information at nursing shift changes: methodological evaluation of speech recognition and information extraction. J Am Med Inform Assoc 2015; 22:e48-66. [PMID: 25336589 PMCID: PMC5901121 DOI: 10.1136/amiajnl-2014-002868] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 08/21/2014] [Accepted: 10/02/2014] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents. METHODS Speech recognition correctness and clinicians' preferences were evaluated using 15 recorder-microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit. RESULTS A noise-cancelling lapel-microphone with a digital voice recorder gave the best correctness (79%). This microphone was also the most preferred option by all but one participant. Although the participants liked the small size of this recorder, their preference was for tablets that can also be used for document proofing and sign-off, among other tasks. Accented speech was harder to recognize than native language and a male speaker was detected better than a female speaker. Information extraction was excellent in filtering out irrelevant text (85% F1) and identifying text relevant to two classes (87% and 70% F1). Similarly to the annotators' disagreements, there was confusion between the remaining three classes, which explains the modest 62% macro-averaged F1. DISCUSSION We present evidence for the feasibility of speech recognition and information extraction to support clinicians' in entering text and unlock its content for computerized decision-making and surveillance in healthcare. CONCLUSIONS The benefits of this automation include storing all information; making the drafts available and accessible almost instantly to everyone with authorized access; and avoiding information loss, delays, and misinterpretations inherent to using a ward clerk or transcription services.
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Affiliation(s)
- Hanna Suominen
- Machine Learning Research Group, NICTA, College of Engineering and Computer Science, The Australian National University, Faculty of Health, University of Canberra, and Department of Information Technology, University of Turku, Canberra, Australian Capital Territory, Australia
| | - Maree Johnson
- Research Faculty of Health Sciences, Australian Catholic University, Sydney, New South Wales, Australia
| | - Liyuan Zhou
- Machine Learning Research Group, NICTA, Canberra, Australian Capital Territory, Australia
| | - Paula Sanchez
- Centre for Applied Nursing Research (University of Western Sydney and South Western Sydney Local Health District), Sydney, New South Wales, Australia
| | - Raul Sirel
- Institute of Estonian and General Linguistics, University of Tartu, Tartu, Estonia
| | - Jim Basilakis
- School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, New South Wales, Australia
| | - Leif Hanlen
- Machine Learning Research Group, NICTA, College of Engineering and Computer Science, The Australian National University, Faculty of Health, University of Canberra, Canberra, Australian Capital Territory, Australia
| | - Dominique Estival
- The MARCS Institute, University of Western Sydney and Department of Linguistics, University of Sydney, Sydney, New South Wales, Australia
| | - Linda Dawson
- Faculty of Social Sciences, University of Wollongong, Wollongong, New South Wales, Australia
| | - Barbara Kelly
- School of Languages and Linguistics, The University of Melbourne, Melbourne, Victoria, Australia
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Hartman DJ. Enhancing and Customizing Laboratory Information Systems to Improve/Enhance Pathologist Workflow. Surg Pathol Clin 2015; 8:137-43. [PMID: 26065788 DOI: 10.1016/j.path.2015.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Optimizing pathologist workflow can be difficult because it is affected by many variables. Surgical pathologists must complete many tasks that culminate in a final pathology report. Several software systems can be used to enhance/improve pathologist workflow. These include voice recognition software, pre-sign-out quality assurance, image utilization, and computerized provider order entry. Recent changes in the diagnostic coding and the more prominent role of centralized electronic health records represent potential areas for increased ways to enhance/improve the workflow for surgical pathologists. Additional unforeseen changes to the pathologist workflow may accompany the introduction of whole-slide imaging technology to the routine diagnostic work.
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Affiliation(s)
- Douglas J Hartman
- Department of Anatomic Pathology, University of Pittsburgh Medical Center, 200 Lothrop Street, A-607, Pittsburgh, PA 15213, USA.
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Abstract
Laboratory information systems (LISs) supply mission-critical capabilities for the vast array of information-processing needs of modern laboratories. LIS architectures include mainframe, client-server, and thin client configurations. The LIS database software manages a laboratory's data. LIS dictionaries are database tables that a laboratory uses to tailor an LIS to the unique needs of that laboratory. Anatomic pathology LIS (APLIS) functions play key roles throughout the pathology workflow, and laboratories rely on LIS management reports to monitor operations. This article describes the structure and functions of APLISs, with emphasis on their roles in laboratory operations and their relevance to pathologists.
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Affiliation(s)
- Walter H Henricks
- Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, L21, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
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Johnson M, Lapkin S, Long V, Sanchez P, Suominen H, Basilakis J, Dawson L. A systematic review of speech recognition technology in health care. BMC Med Inform Decis Mak 2014; 14:94. [PMID: 25351845 PMCID: PMC4283090 DOI: 10.1186/1472-6947-14-94] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 10/02/2014] [Indexed: 11/22/2022] Open
Abstract
Background To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. Methods A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. Results The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. Conclusions SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.
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Affiliation(s)
- Maree Johnson
- Faculty of Health Sciences, Australian Catholic University, 40 Edward Street, 2060 North Sydney, NSW, Australia.
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Text mining and information analysis of health documents. Artif Intell Med 2014; 61:127-30. [PMID: 24998391 DOI: 10.1016/j.artmed.2014.06.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Accepted: 06/04/2014] [Indexed: 11/24/2022]
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Chan P, Thyparampil PJ, Chiang MF. Accuracy and speed of electronic health record versus paper-based ophthalmic documentation strategies. Am J Ophthalmol 2013; 156:165-172.e2. [PMID: 23664152 DOI: 10.1016/j.ajo.2013.02.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Revised: 02/16/2013] [Accepted: 02/21/2013] [Indexed: 11/18/2022]
Abstract
PURPOSE To compare accuracy and speed of keyboard and mouse electronic health record (EHR) documentation strategies with those of a paper documentation strategy. DESIGN Prospective cohort study. METHODS Three documentation strategies were developed: (1) keyboard EHR, (2) mouse EHR, and (3) paper. Ophthalmology trainees recruited for the study were presented with 5 clinical cases and documented findings using each strategy. For each case-strategy pair, findings and documentation time were recorded. Accuracy of each strategy was calculated based on sensitivity (fraction of findings in actual case that were documented by subject) and positive ratio (fraction of findings identified by subject that were present in the actual case). RESULTS Twenty subjects were enrolled. A total of 258 findings were identified in the 5 cases, resulting in 300 case-strategy pairs and 77 400 possible total findings documented. Sensitivity was 89.1% for the keyboard EHR, 87.2% for mouse EHR, and 88.6% for the paper strategy (no statistically significant differences). The positive ratio was 99.4% for the keyboard EHR, 98.9% for mouse EHR, and 99.9% for the paper strategy (P < .001 for mouse EHR vs paper; no significant differences between other pairs). Mean ± standard deviation documentation speed was significantly slower for the keyboard (2.4 ± 1.1 seconds/finding) and mouse (2.2 ± 0.7 seconds/finding) EHR compared with the paper strategy (2.0 ± 0.8 seconds/finding). Documentation speed of the mouse EHR strategy worsened with repetition. CONCLUSIONS No documentation strategy was perfectly accurate in this study. Documentation speed for both EHR strategies was slower than with paper. Further studies involving total physician time requirements for ophthalmic EHRs are required.
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Affiliation(s)
- Patrick Chan
- Department of Ophthalmology, Harkness Eye Institute, Columbia University College of Physicians and Surgeons, New York, NY, USA
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Hansel DE, Miller JS, Cookson MS, Chang SS. Challenges in the pathology of non-muscle-invasive bladder cancer: a dialogue between the urologic surgeon and the pathologist. Urology 2013; 81:1123-30. [PMID: 23522296 DOI: 10.1016/j.urology.2013.01.027] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2012] [Revised: 01/16/2013] [Accepted: 01/18/2013] [Indexed: 11/25/2022]
Abstract
Approximately 70%-80% of urothelial carcinomas are detected at the stage of non-muscle-invasive bladder cancer (NMIBC). Initial management is often successful, but recurrence is common and leads to a long, burdensome, costly disease course. The quality and efficiency of care depends in part on accurate, clearly communicated descriptions of tumor characteristics. This review identifies current best practices, unmet needs, and key issues in the pathology of NMIBC for the practicing urologist. Reasonable and objective recommendations are provided with the goal of improving urologist-pathologist communication, the efficiency of healthcare utilization, and outcomes for patients with NMIBC.
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