PDF herunterladen
CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 056-064
DOI: 10.1055/s-0039-1677913
Section 1: Health Information Management
Survey
Georg Thieme Verlag KG Stuttgart

Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management

Mary H. Stanfill
1   United Audit Systems, Inc., Cincinnati, OH, USA
,
David T. Marc
2   The College of St. Scholastica, Department of Health Informatics and Information Management, Duluth, MN, USA
› Institutsangaben
Weitere Informationen

Publikationsverlauf

Publikationsdatum:
16. August 2019 (online)

   Lizenzen und Reprints

Summary

Objective: This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals. Methods: A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email.

Results: The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Fbtient privacy and confidentiality; and 4) HIM workforce training and education.

Discussion: HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional’s ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use.

Conclusions: AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.

Keywords

Artificial Intelligence - Health Information Management - automation - medical coding - health workforce
 

  • References

  • 1 AHRQ. Medal.org (Medical Algorithms): Agency for Healthcare Research and Quality, Innovation Exchange; 2011 [updated November 9, 2011; cited 2018 December 10]. Available from: https://innovations.ahrq.gov/qualitytools/medalorg-medical-algorithms
    PubMed
  • 2 Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J. , et al; Canadian Association of Radiologists. White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J 2018; 69 (02) 120-35
    PubMedGoogle Scholar
  • 3 Trump DJ. Executive order on maintaining American leadership in artificial intelligence. In: President EOot, editor. Federal Register: White House; 2019. p. 3967-72
    PubMed
  • 4 Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial Intelligence Transforms the Future of Health Care. Am J Med 2019
    PubMedGoogle Scholar
  • 5 Ganapathy K, Abdul SS, Nursetyo AA. Artificial intelligence in neurosciences: A clinician’s perspective. Neurol India 2018; 66 (04) 934-9
    CrossrefPubMedGoogle Scholar
  • 6 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S. , et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2 (04) 230-43
    CrossrefPubMedGoogle Scholar
  • 7 Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M. , et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol 2018; 71 (23) 2668-79
    CrossrefPubMedGoogle Scholar
  • 8 eHealth Initiative. The Machines are Here: The Influence of AI on Healthcare. EHIDC; 2018
    PubMed
  • 9 Pesapane F, Volonte C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 2018; 9 (05) 745-53
    CrossrefPubMedGoogle Scholar
  • 10 Collier M, Fu R, Yin L, Christiansen P. Artificial Intelligence (AI): Healthcare’s New Nervous System; 2017
    PubMed
  • 11 Miriovsky BJ, Shulman LN, Abernethy AP. Importance of health information technology, electronic health records, and continuously aggregating data to comparative effectiveness research and learning health care. J Clin Oncol 2012; 30 (34) 4243-8
    CrossrefPubMedGoogle Scholar
  • 12 Mesko B, Hetenyi G, Gyorffy Z. Will artificial intelligence solve the human resource crisis in healthcare?. BMC Health Serv Res 2018; 18 (01) 545
    CrossrefPubMedGoogle Scholar
  • 13 Hurd H, Kelly R. Rise of the Machines: Artificial Intelligence and its Growing Impact on U.S. Policy. https://oversight.house.gov/wp-content/uploads/2018/09/AI-White-Paper.pdf : Subcommittee on Information Technology Committee on Oversight and Government Reform; 2018
    PubMed
  • 14 Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 2016; 375 (13) 1216-9
    CrossrefPubMedGoogle Scholar
  • 15 Dimick C. Health information management 2025. J AHIMA 2012; 83 (08) 24-31
    PubMedGoogle Scholar
  • 16 Stanfill MH, Williams M, Fenton SH, Jender RA, Hersh WR. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc 2010; 17 (06) 646-51
    CrossrefPubMedGoogle Scholar
  • 17 Workgroup AC-ACe-H. Delving into computer-assisted coding. J AHIMA 2004; 75 (10) 48A-H
    PubMedGoogle Scholar
  • 18 Research and Markets Report. Computer Assisted Coding Market by Software (NLP, Structured input, Integrated Systems), Service, Application (Automated Encoding, Clinical Coding Audit, Management Reporting) - Global Forecast to 2022. Research and Markets; 2017
    PubMed
  • 19 Dougherty M, Seabold S, White SE. Study reveals hard facts on CAC. J AHIMA 2013; 84 (07) 54-6
    PubMedGoogle Scholar
  • 20 Crawford M. The truth about computer-assisted coding. J AHIMA 2013; 84 (07) 24-7
    PubMedGoogle Scholar
  • 21 Comfort A, D’Amato C, Isom C, Rihanek T, Thomas-Flowers D. Automated Coding Workflow and CAC Practice Guidance (2013 update). AHIMA Practice Brief 2013
    PubMed
  • 22 O’Dowd E. HIT Infrastructure: HIT Infrastructure; 2018. [cited 2018]. Available from: https://hitinfrastructure.com/news/computer-assisted-coding-systems-help-orgs-handle-increasing-data
    PubMed
  • 23 Butler M. Computer-Assisted Coding Reality Check. J AHIMA 2019; 90 (06) 10-3
    PubMedGoogle Scholar
  • 24 Eramo LA. J AHIMA 2017. [cited 2019]. Available from: https://journal.ahima.org/2017/06/01/has-cac-lived-up-to-its-promise-providers-vendors-weigh-in/
    PubMed
  • 25 Curtiss ET, Susan E. Computer Assisted Coding: Market Shares, Strategies, and Forecasts, Worldwide 2016 to 2022. WinterGreen Research; 2017
    PubMed
  • 26 Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19 (06) 1236-46
    CrossrefPubMedGoogle Scholar
  • 27 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM. , et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542 (7639): 115-8
    CrossrefPubMedGoogle Scholar
  • 28 King Jr BF. Artificial Intelligence and Radiology: What Will the Future Hold?. J Am Coll Radiol 2018; 15 (3 Pt B): 501-3
    CrossrefPubMedGoogle Scholar
  • 29 CMS. ICD-10-CM Official Guidelines for Coding and Reporting FY 2019. CDC; 2018
    PubMed
  • 30 NHS Digital. National Clinical Coding Standards ICD-10 5th Edition. NHS; 2018
    PubMed
  • 31 Canadian Institute for Health Information. Canadian Coding Standards for Version 2018 ICD-10-CA and CCI. CIHI; 2018
    PubMed
  • 32 Saqib M, Sha Y, Wang MD. Early Prediction of Sepsis in EMR Records Using Traditional ML Techniques and Deep Learning LSTM Networks. Conf Proc IEEE Eng Med Biol Soc 2018; 2018: 4038-41
    PubMedGoogle Scholar
  • 33 Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T. , et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak 2018; 18 (01) 44
    CrossrefPubMedGoogle Scholar
  • 34 Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg 2018; 268 (04) 574-83
    CrossrefPubMedGoogle Scholar
  • 35 HL7. FHIR Release 3 HL7.org2017 [cited 2018 December 10]. Available from: https://hl7.org/implement/standards/fhir/codesystem-condition-ver-status.html
    PubMed
  • 36 Cohn J. The Health Tech Report: Robot Will See You Now. The Atlantic 2013(March):59-67
    PubMed
  • 37 Metz R. The smartphone app that can tell you’re depressed before you know it yourself: MIT Technology Review; 2018 [cited 2018 December 10]. Available from: https://www.technologyreview.com/s/612266/the-smartphone-app-that-can-tell-youre-depressed-before-you-know-it-yourself/
    PubMed
  • 38 Sawh M. The best smart clothing: From biometric shirts to contactless paymentjackets: Smart fabrics and garments on sale now or hitting the shops soon Wareable.com2018 [cited 2019 January 14]. Available from: https://www.wareable.com/smart-clothing/best-smart-clothing
    PubMed
  • 39 Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar S, Thota C. A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gener Comput Syst 2018; 82: 375-87
    CrossrefPubMedGoogle Scholar
  • 40 Bandyopadhyay D, Sen J. Internet ofthings: Applications and challenges in technology and standardization. Wirel Pers Commun 2011; 58 (01) 49-69
    CrossrefPubMedGoogle Scholar
  • 41 The Open Group. Open Data Element Framework (O-DEF) Version 1.0: The Open Group; 2016 [cited 2019 January 9]. Available from: https://publications.opengroup.org/c163
    PubMed
  • 42 W3C. Semantic Web: W3C; 2017 [cited 2019 January 9]. Available from: https://www.w3.org/standards/semanticweb/
    PubMed
  • 43 Empel S. The way forward. AHIMA develops information governance principles to lead healthcare toward better data management. J AHIMA 2014; 85 (10) 30-2 ; quiz 4
    PubMedGoogle Scholar
  • 44 eHealth Initiative. Artificial Intelligence in Healthcare. EHIDC; 2018
    PubMed
  • 45 NHS Health Research Authority. Confidentiality Advisory Group: NHS; 2018 [cited 2018 December 14]. Available from: https://www.hra.nhs.uk/approvals-amendments/what-approvals-do-i-need/confidentiality-advisory-group/
    PubMed
  • 46 U.S. Department of Health & Human Services. The HIPAA Privacy Rule: HHS; 2015 [cited 2018 December 14]. Available from: https://www.hhs.gov/hipaa/for-professionals/privacy/index.html
    PubMed
  • 47 Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl) 2017; 7 (04) 351-67
    CrossrefPubMedGoogle Scholar
  • 48 Price N. Artificial Intelligence in Health Care: Applications and Legal Issues. The Sci Tech Lawyer 2017; 14 (01) 10-3
    PubMedGoogle Scholar
  • 49 Hoyle P. Health Information is central to changes in healthcare: A clinician’s view. Health Inf Manag 2018; 48 (01) 48-51
    PubMedGoogle Scholar
  • 50 Shepheard J. Ethical leadership and why health information management professionals need to be involved. Commentary on Health Information is central to changes in healthcare: a clinician’s view. Health Inf Manag 2018;2018
    PubMed
  • 51 Sandefer R, Marc D, Mancilla D, Hamada D. Survey Predicts Future HIM Workforce Shifts. J AHIMA 2015; 86 (07) 32-5
    PubMedGoogle Scholar
  • 52 Riley M, Robinson K, Prasad N, Gleeson B, Barker E, Wollersheim D. , et al. Workforce survey of Australian graduate health information managers: Employability, employment, and knowledge and skills used in the workplace. Health Inf Manag 2019
    PubMed
  • 53 Sandefer R. New Market Research Supports HIM Reimagined’s Call for Professional Adaptation. J AHIMA 2018; 89 (05) 20-3
    PubMedGoogle Scholar
  • 54 Marc DT, Robertson J, Gordon L, Green-Lawson ZD, Gibbs D, Dover K. , et al. What the Data Say About HIM Professional Trends. J AHIMA 2017; 88 (05) 25-31
    PubMedGoogle Scholar
  • 55 Sher ML, Talley PC, Cheng TJ, Kuo KM. How can hospitals better protect the privacy of electronic medical records? Perspectives from staff members of health information management departments. Health Inf Manag 2017; 46 (02) 87-95
    PubMedGoogle Scholar
  • 56 Delahanty RJ, Kaufman D, Jones SS. Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients. Crit Care Med 2018; 46 (06) e481-e8
    CrossrefPubMedGoogle Scholar
  • 57 Davies AFD, Gorbis M. Future Work Skills 2020. Institute for the Future, University of Pheonix Research Institute; 2011
    PubMed