استخراج نظام‌مند آیتم‌های داده‌ای لازم برای تشخیص بارداری‌های شایع پرخطر با استفاده از رویکرد دلفی

نوع مقاله: اصیل پژوهشی

نویسندگان

1 دانشجوی دکترای تخصصی انفورماتیک پزشکی، کمیته تحقیقات دانشجویی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران.

2 دانشیار گروه زنان و مامایی، مرکز تحقیقات سلامت زنان، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران.

3 استادیار گروه زنان و مامایی، مرکز تحقیقات سلامت زنان، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران.

4 استادیار گروه انفورماتیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران.

چکیده

مقدمه: در زمان مشاوره تلفنی رزیدنت - استاد، کیفیت تصمیمات بالینی پزشک آنکال وابستگی شدیدی به کیفیت اطلاعات دریافتی از رزیدنت دارد. برخی فاکتورها از جمله نوع، تعداد، قالب، کیفیت و حجم داده­های مورد مبادله می‌تواند بر کیفیت مشاوره از راه دور تأثیر بگذارد. بنابراین طراحی یک مدل استاندارد مورد اعتماد در این‌گونه ارتباطات بالینی، ضروری به نظر می­رسد. لذا مطالعه حاضر با هدف طراحی آرکه­تایپ داده (داده‌سازه یا داده ساختار) بالینی مورد نیاز جهت تصمیم‌گیری از راه دور در حیطه بارداری‌های شایع پرخطر انجام شد.
روش‌کار: این مطالعه مقطعی چند مرحله­ای با به‌کارگیری روش دلفی برای شناسایی آیتم‌های تشخیصی بارداری‌های شایع پرخطر به منظور طراحی آرکه­تایپ تصمیم‌گیری بالینی در سه دپارتمان تخصصی زنان و زایمان بیمارستان‌های آموزشی مشهد انجام شد.
یافته­ها: 5 بارداری پرخطر شایع (منجر به زایمان) شامل: فشارخون بالا، خونریزی­های سه ماهه سوم، پارگی کیسه آب، زایمان زودرس و دیررس بودند. پس از مرور منابع، 161 گروه/ آیتم اطلاعاتی برای این بارداری­های پرخطر یافت شد که پس از بررسی نظرات متخصصین، 158 آیتم از آن‌ها باقی مانده و در 5 طبقه اطلاعات عمومی، شکایت و شرح حال فعلی، تاریخچه پزشکی، معاینات بالینی، و نتایج پاراکلینیک طبقه­بندی شدند.
نتیجه­گیری: نتایج مطالعه حاضر نشان داد که می­توان از تعامل نزدیک پزشکان بالینی با متخصصین انفورماتیک پزشکی جهت تسهیل در استخراج آیتم‌های اطلاعاتی مورد نیاز جهت بهبود فرآیندهای مشاوره بهره برد.

کلیدواژه‌ها


عنوان مقاله [English]

Systematic extraction of diagnostic data items for common high-risk pregnancies using Delphi technique

نویسندگان [English]

  • Kolsoum Deldar 1
  • Fatemeh Tara 2
  • Sara Mirzaeian 3
  • Seyeheh Azam Pourhoseini 3
  • Seyed Mahmood Tara 4
1 PhD candidate of Medical Informatics, Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
2 Associate professor, Women's Health Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
3 Assistant professor, Women's Health Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
4 Assistant professor, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
چکیده [English]

Introduction: The quality of clinical decisions made by on-call physician is totally dependent on the quality of medical information received from resident. Some factors such as type, number, format, quality and also the volume of such information may highly affect the quality of remote consultations. Therefore, developing a trusted standard model for such clinical communication seems to be necessary. This study was conducted with aim to design a clinical archetype (structure data) for remote decision making in high-risk pregnancies.
Methods: This multi-stage cross-sectional study was conducted by using Delphi technique for identifying of the most common high-risk pregnancies to design a archetype for clinical decision making in three obstetrics and gynecology departments of educational hospitals, Mashhad.
Results: There were 5 common high-risk pregnancies (leading to delivery) including hypertension, third trimester hemorrhage, PROM, pre-term and post-term delivery. 161 clinically-important groups / items were extracted from scientific references and then hand-filtered to 158 items by the participating gynecology and obstetrics experts. The final items were categorized into five classes including general information, chief complaint / current problem, medical history, clinical examination, and paraclinic results.
Conclusion: Our findings showed that close interaction between clinicians and specialists in medical informatics may facilitate the improvement process of medical teleconsultations.

کلیدواژه‌ها [English]

  • Clinical archetype
  • Delphi technique
  • High-risk pregnancy
  • Remote consultation
  1. James DK, Steer PJ, Weiner CP, Gonik B. High risk pregnancy: management options-expert consult. 4th ed. Philadelphia, PA: Elsevier Health Sciences; 2010.
  2. Behruzi R, Hatem M, Goulet L, Fraser W, Leduc N, Misago C. Humanized birth in high risk pregnancy: barriers and facilitating factors. Med Health Care Philos 2010; 13(1):49-58.
  3. Reece EA, Lequizamon G, Silva J, Whiteman V, Smith D. Intensive interventional maternity care reduces infant morbidity and hospital costs. J Maternal Fetal Neonatal Med 2002; 11(3):204-10.
  4. Farrokh Islamloui HR, Nan Bakhsh F, Heshmati F, Amirabi A. Epidemiological of maternal mortality in the west Azerbaijan province (2001-2005). J Urmia Med Sci 2006; 17(1):23-31. (Persian).
  5. Luo ZC, Liu S, Wilkins R, Kramer MS. Risks of stillbirth and early neonatal death by day of week. CMAJ 2004; 170(3):337-41.
  6. Rautava L, Lehtonen L, Peltola M, Korvenranta E, Korvenranta H, Linna M, et al. The effect of birth in secondary-or tertiary-level hospitals in Finland on mortality in very preterm infants: a birth-register study. Pediatrics 2007; 119(1):e257-63.
  7. Stephansson O, Dickman PW, Johansson AL, Kieler H, Cnattingius S. Time of birth and risk of intrapartum and early neonatal death. Epidemiology 2003; 14(2):218-22.
  8. Salihu HM, Ibrahimou B, August EM, Dagne G. Risk of infant mortality with weekend versus weekday births: a population-based study. J Obstet Gynaecol Res 2012; 38(7):973-9.
  9. Dadipoor S, Madani A, Alavi A, Roozbeh N, Safari Moradabadi A. A survey of the growing trend of caesarian section in Iran and the world: a review article. Iran J Obstet Gynecol Infertil 2016; 19(27):8-17. (Persian).
  10. Pasupathy D, Wood AM, Pell JP, Fleming M, Smith GC. Time of birth and risk of neonatal death at term: retrospective cohort study. BMJ 2010; 341:c3498.
  11. Frank AU. Analysis of dependence of decision quality on data quality. J Geograph Sys 2008; 10(1):71-88.
  12. Resnicow K, Abrahamse P, Tocco RS, Hawley S, Griggs J, Janz N, et al. Development and psychometric properties of a brief measure of subjective decision quality for breast cancer treatment. BMC Med Inform Decis Mak 2014; 14:110.
  13. Raghunathan S. Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decis Support Sys 1999; 26(4):275-86.
  14. Hwang MI, Lin JW. Information dimension, information overload and decision quality. J Inform Sci 1999; 25(3):213-8.
  15. Bodenheimer T. Coordinating care--a perilous journey through the health care system. N Engl J Med 2008; 358(10):1064-71.
  16. O’Malley AS, Tynan A, Cohen GR, Kemper N, Davis MM. Coordination of care by primary care practices: strategies, lessons and implications. Res Brief 2009; 12:1-16.
  17. Mehrotra A, Forrest CB, Lin CY. Dropping the baton: specialty referrals in the United States. Milbank Q 2011; 89(1):39-68.
  18. Deldar K, Bahaadinbeigy K, Tara SM. Teleconsultation and clinical decision making: a systematic review. Acta Inform Med 2016; 24(4):286-92.
  19. Martinez-Costa C, Menarguez-Tortosa M, Fernandez-Breis JT, Maldonado JA. A model-driven approach for representing clinical archetypes for Semantic Web environments. J Biomed Inform 2009; 42(1):150-64.
  20. Santori G, Valente R, Cambiaso F, Ghirelli R, Gianelli Castiglione A, Valente U. Preliminary results of an expert-opinion elicitation process to prioritize an informative system funded by Italian Ministry of Health for cadaveric donor management, organ allocation, and transplantation activity. Transplant Proc 2004; 36(3):433-4.
  21. Keeney S, Hasson F, McKenna HP. A critical review of the Delphi technique as a research methodology for nursing. Int J Nurs Stud 2001; 38(2):195-200.
  22. Eibling D, Fried M, Blitzer A, Postma G. Commentary on the role of expert opinion in developing evidence‐based guidelines. Laryngoscope 2014; 124(2):355-7.
  23. Shariat SV, Asad EA, Alirezaie N, Bashar DZ, Birashk B, Tehrani DM, et al. Age rating of computer games from a psychological perspective: a delfi study. Adv Cognitive Sci 2009; 11(2):8-18. (Persian).
  24. Ramphal SR, Moodley J. Emergency gynaecology. Best Pract Res Clin Obstet Gynaecol 2006; 20(5):729-50.
  25. Shershneva MB, Carnes M, Bakken LL. A model of teaching-learning transactions in generalist-specialist consultations. J Contin Educ Health Prof 2006; 26(3):222-9.
  26. Nerlich M, Balas EA, Schall T, Stieglitz SP, Filzmaier R, Asbach P, et al. Teleconsultation practice guidelines: report from G8 Global Health Applications Subproject 4. Telemed J E Health 2002; 8(4):411-8.
  27. Henn P, Power D, Smith SD, Power T, Hynes H, Gaffney R, et al. A metric-based analysis of structure and content of telephone consultations of final-year medical students in a high-fidelity emergency medicine simulation. BMJ Open 2012; 2(5):e001298.
  28. Patterson V, Wootton R. A web-based telemedicine system for low-resource settings 13 years on: insights from referrers and specialists. Glob Health Action 2013; 6(1):21465.
  29. AtalaĞ K. Archetype based domain modeling for health information systems. [Doctoral Dissertation]. Ankara: Middle East Technical University; 2007.
  30. Patterson V, Wootton R. A web-based telemedicine system for low-resource settings 13 years on: insights from referrers and specialists. Glob Health Action 2013; 6(1):21465.
  31. Ye K, McD Taylor D, Knott JC, Dent A, MacBean CE. Handover in the emergency department: Deficiencies and adverse effects. Emerg Med Australas 2007; 19(5):433-41.
  32. Kessler CS, Afshar Y, Sardar G, Yudkowsky R, Ankel F, Schwartz A. a prospective, randomized, controlled study demonstrating a novel, effective model of transfer of care between physicians: the 5 Cs of consultation. Acad Emerg Med 2012; 19(8):968-74.
  33. Marshall SD, Harrison JC, Flanagan B. Telephone referral education, and evidence of retention and transfer after six-months. BMC Med Educ 2012; 12:38
  34. Marshall S, Harrison J, Flanagan B. The teaching of a structured tool improves the clarity and content of interprofessional clinical communication. Qual Saf Health Care 2009; 18(2):137-40.
  35. Westerman RF, Hull FM, Bezemer PD, Gort G. A study of communication between general practitioners and specialists. Br J Gen Pract 1990; 40(340):445-9.
  36. van Steenkiste BC, Jacobs JE, Verheijen NM, Levelink JH, Bottema BJ. A Delphi technique as a method for selecting the content of an electronic patient record for asthma. Int J Med Inform 2002; 65(1):7-16.