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Appl Clin Inform 2021; 12(05): 1157-1160
DOI: 10.1055/s-0041-1740259
Special Section on Workflow Automation

Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery

Kevin Yang
1   Department of Dermatology, Tufts University School of Medicine, Boston, Massachusetts, United States
,
Vinod E. Nambudiri
2   Department of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts, United States
› Author Affiliations Funding None.
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Workflow automation involves utilizing an array of technologies to facilitate the completion of specific daily tasks. In the business and financial sectors, the implementation of automation has had transformative and beneficial effects including improved quality of services, lower costs, and expanded accessibility. While workflow automation has begun to enter health care, the introduction has been slow, leaving much room for optimization. In health care, daily workflow for physicians may involve handling electronic health records (EHRs), administrative tasks, patient coordination, and researching of clinical evidence. Clerical burdens and administrative tasks are commonly cited as factors that contribute to physician burnout and frustration, which may lead to reduced time for patient interaction, decreased career satisfaction, and diminished delivery of high-quality care.[1] [2] [3] In this article, we explore areas of workflow within ambulatory, outpatient health care that would potentially benefit from the implementation of automation. We also propose potential solutions to encourage more efficient outcomes in administrative and clinical practice workflows, which have the ability to enhance the delivery of more humanistic medical care.

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in the project.




Publication History

Received: 10 June 2021

Accepted: 21 October 2021

Article published online:
29 December 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med 2018; 283 (06) 516-529
    CrossrefPubMedGoogle Scholar
  • 2 Gardner RL, Cooper E, Haskell J. et al. Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019; 26 (02) 106-114
    CrossrefPubMedGoogle Scholar
  • 3 Robertson SL, Robinson MD, Reid A. Electronic health record effects on work-life balance and burnout within the I3 population collaborative. J Grad Med Educ 2017; 9 (04) 479-484
    CrossrefPubMedGoogle Scholar
  • 4 Wu DTY, Xu C, Kim A, Bindhu S, Mah KE, Eckman MH. A scoping review of health information technology in clinician burnout. Appl Clin Inform 2021; 12 (03) 597-620
    Article in Thieme ConnectPubMedGoogle Scholar
  • 5 van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW. The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med 2021; 4 (01) 57
    CrossrefPubMedGoogle Scholar
  • 6 Kaufman DR, Sheehan B, Stetson P. et al. Natural language processing-enabled and conventional data capture methods for input to electronic health records: a comparative usability study. JMIR Med Inform 2016; 4 (04) e35
    CrossrefPubMedGoogle Scholar
  • 7 Kostrinsky-Thomas AL, Hisama FM, Payne TH. Searching the PDF haystack: automated knowledge discovery in scanned EHR documents. Appl Clin Inform 2021; 12 (02) 245-250
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 Afzal M, Alam F, Malik KM, Malik GM. Clinical context-aware biomedical text summarization using deep neural network: model development and validation. J Med Internet Res 2020; 22 (10) e19810
    CrossrefPubMedGoogle Scholar
  • 9 Laï MC, Brian M, Mamzer MF. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med 2020; 18 (01) 14
    CrossrefPubMedGoogle Scholar
  • 10 Wang Y, Wang L, Rastegar-Mojarad M. et al. Clinical information extraction applications: a literature review. J Biomed Inform 2018; 77: 34-49
    CrossrefPubMedGoogle Scholar
  • 11 Mishra R, Bian J, Fiszman M. et al. Text summarization in the biomedical domain: a systematic review of recent research. J Biomed Inform 2014; 52: 457-467
    CrossrefPubMedGoogle Scholar
  • 12 Garg S, Williams NL, Ip A, Dicker AP. Clinical integration of digital solutions in health care: an overview of the current landscape of digital technologies in cancer care. JCO Clin Cancer Inform 2018; 2: 1-9
    Google Scholar
  • 13 Mass General Brigham COVID-19 Screener. Accessed August 1, 2021 at: https://covidscreener.massgeneralbrigham.org
    PubMedGoogle Scholar
  • 14 Facilities CDC. COVID-19 Screening. Updated November 12, 2020. Accessed May 18, 2021 at: https://www.cdc.gov/screening/index.html
    PubMedGoogle Scholar
  • 15 Palanica A, Docktor MJ, Lieberman M, Fossat Y. The need for artificial intelligence in digital therapeutics. Digit Biomark 2020; 4 (01) 21-25
    CrossrefPubMedGoogle Scholar
  • 16 Fulmer R, Joerin A, Gentile B, Lakerink L, Rauws M. Using psychological artificial intelligence (TESS) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health 2018; 5 (04) e64
    CrossrefPubMedGoogle Scholar
  • 17 Gabrielli S, Rizzi S, Bassi G. et al. Engagement and effectiveness of a healthy-coping intervention via chatbot for university students during the COVID-19 pandemic: mixed methods proof-of-concept study. JMIR Mhealth Uhealth 2021; 9 (05) e27965
    CrossrefPubMedGoogle Scholar
  • 18 Schreier DJ, Lovely JK. Optimizing clinical monitoring tools to enhance patient review by pharmacists. Appl Clin Inform 2021; 12 (03) 621-628
    Article in Thieme ConnectPubMedGoogle Scholar
  • 19 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
    CrossrefPubMedGoogle Scholar
  • 20 Ely JW, Osheroff JA, Chambliss ML, Ebell MH, Rosenbaum ME. Answering physicians' clinical questions: obstacles and potential solutions. J Am Med Inform Assoc 2005; 12 (02) 217-224
    CrossrefPubMedGoogle Scholar
  • 21 Willis M, Duckworth P, Coulter A, Meyer ET, Osborne M. Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice. BMJ Open 2020; 10 (06) e032412
    CrossrefPubMedGoogle Scholar
  • 22 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542 (7639): 115-118
    CrossrefPubMedGoogle Scholar
  • 23 Workman TE, Fiszman M, Hurdle JF. Text summarization as a decision support aid. BMC Med Inform Decis Mak 2012; 12: 41
    CrossrefPubMedGoogle Scholar
  • 24 Stai B, Heller N, McSweeney S. et al. Public perceptions of artificial intelligence and robotics in medicine. J Endourol 2020; 34 (10) 1041-1048
    CrossrefPubMedGoogle Scholar
  • 25 Nelson CA, Pérez-Chada LM, Creadore A. et al. Patient perspectives on the use of artificial intelligence for skin cancer screening: a qualitative study. JAMA Dermatol 2020; 156 (05) 501-512
    CrossrefPubMedGoogle Scholar
  • 26 Sarwar S, Dent A, Faust K. et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med 2019; 2: 28
    CrossrefPubMedGoogle Scholar
  • 27 Asan O, Bayrak AE, Choudhury A. Artificial intelligence and human trust in healthcare: focus on clinicians. J Med Internet Res 2020; 22 (06) e15154
    CrossrefPubMedGoogle Scholar