Completeness and accuracy of data is probably a persistent and intrusive problem in any process related to data capture. This is especially true in the clinical field, where omitting significant information can have considerable implications for diagnosis and treatment in general. History taking from patients represents a crucial phase for physicians in order to evaluate the patient’s wellness status and to perform correct diagnoses. As a routine procedure, it is a time-consuming and not so appealing obligation. In this paper we present a smart approach to anamnesis in order to gain as much data as possible and to have high quality information by avoiding any misunderstandings or errors. The approach is mainly based on the possibility to capture data directly at the source increasing the overall effectiveness of physician’s time and of the visit itself. Its feasibility has been evaluated in the context of a complex clinical domain: maternal and fetal assessment during pregnancy.


Medical history taking Data incompleteness Data quality Data capture Maternal and fetal assessment 


  1. 1.
    Engel, G.E., Morgan, W.L.: Interviewing and Patient Care. Saunders, Philadelphia (1973)Google Scholar
  2. 2.
    Roshan, M., Rao, A.P.: A study on relative contributions of the history, physical examination and investigations in making medical diagnosis. J. Assoc. Phys. India 48(8), 771–775 (2000)Google Scholar
  3. 3.
    Peterson, M.C., Holbrook, J.H., Von Hales, D., Smith, N.L., Staker, L.V.: Contributions of the history, physical examination, and laboratory investigation in making medical diagnoses. West. J. Med. 156(2), 163–165 (1992)Google Scholar
  4. 4.
    Keifenheim, K.E., Teufel, M., Ip, J., Speiser, N., Leehr, E.J., Zipfel, S., Herrmann-Werner, A.: Teaching history taking to medical students: a systematic review. BMC Med. Educ. 15, 159 (2015)CrossRefGoogle Scholar
  5. 5.
    Davenport, S., Goldberg, D., Millar, T.: How psychiatric disorders are missed during medical consultations. Lancet 2, 439–441 (1987)CrossRefGoogle Scholar
  6. 6.
    Palermo, T.M., Valenzuela, D., Stork, P.P.: A randomized trial of electronic versus paper pain diaries in children: impact on compliance, accuracy, and acceptability. Pain 107, 213–219 (2004)CrossRefGoogle Scholar
  7. 7.
    Pringle, M.: Preventing ischaemic heart disease in one general practice: from one patient, through clinical audit, needs assessment, and commissioning into quality improvement. Br. Med. J. 317(7166), 1120–1123 (1998). discussion 1124CrossRefGoogle Scholar
  8. 8.
    Mayne, J.G., Weksel, W., Sholtz, P.N.: Toward automating the medical history. Mayo Clinic Proc. 43(1), 1–25 (1968)Google Scholar
  9. 9.
    Pappas, Y., Anandan, C., Liu, J., Car, J., Sheikh, A., Majeed, A.: Computer-assisted history-taking systems (CAHTS) in health care: benefits, risks and potential for further development. Inform. Prim. Care. 19(3), 155–160 (2011)Google Scholar
  10. 10.
    Bowling, A.: Mode of questionnaire administration can have serious effects on data quality. J. Publ. Health 27(3), 281–291 (2005)CrossRefGoogle Scholar
  11. 11.
    Cash-Gibson, L., Pappas, Y., Car, J.: Computer-assisted versus oral-and-written history taking for the management of cardiovascular disease (Protocol). Cochrane Database Syst. Rev. 3, Art. no. CD009751 (2012)Google Scholar
  12. 12.
    Tiplady, B.A., Crompton, G.K., Dewar, M.H., Böllert, F.G.E., Matusiewicz, S.P., Campbell, L.M., Brackenridge, D.: The use of electronic diaries in respiratory studies. Ther. Innov. Regulatory Sci. 31(3), 759–764 (1997)Google Scholar
  13. 13.
    Gaertner, J., Elsner, F., Pollmann-Dahmen, K., Radbruch, L., Sabatowski, R.: Electronic pain diary: a randomized crossover study. J. Pain Symptom Manage. 28(3), 259–267 (2004)CrossRefGoogle Scholar
  14. 14.
    Lauritsen, K., Degl’, Innocenti A., Hendel, L., Praest, J., Lytje, M.F., Clemmensen-Rotne, K., Wiklund, I.: Symptom recording in a randomised clinical trial: paper diaries vs. electronic or telephone data capture. Control. Clin. Trials 25(6), 585–597 (2004)CrossRefGoogle Scholar
  15. 15.
    Bulpitt, C.J., Beilin, L.J., Coles, E.C., Dollery, C.T., Johnson, B.F., Munro-Faure, A.D., Turner, S.C.: Randomised controlled trial of computer-held medical records in hypertensive patients. Br. Med. J. 1(6011), 677–679 (1976)CrossRefGoogle Scholar
  16. 16.
    Vaira, L., Bochicchio, M.A., Navathe, S.B.: Perspectives in healthcare data management with application to maternal and fetal wellbeing. In: 24th Italian Symposium on Advanced Database Systems (SEBD 2016), Ugento, Lecce, 19–22 June 2016 (2016)Google Scholar
  17. 17.
    Bochicchio, M.A., Vaira, L.: Fetal growth: where are data? It’s time for a new approach. Int. J. Biomed. Healthc. 4(1), 18–22 (2016)Google Scholar
  18. 18.
    Vaira, L., Bochicchio, M.A.: Can ICT help to solve the clinical appropriateness problem? An experience in the Italian public health. J. Commun. Comput. 12(6), 303–310 (2015)Google Scholar
  19. 19.
    Bochicchio, M.A., Vaira, L.: Are static fetal growth charts still suitable for diagnostic purposes? In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Belfast, UK, 2–5 November 2014 (2014). doi: 10.1109/BIBM.2014.6999260

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© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Set-Lab, Department of Engineering for InnovationUniversity of SalentoLecceItaly

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