Pre-consultation history taking systems and their impact on modern practices: Advantages and limitations

Gulnur Zhakhina 1 2, Karina Tapinova 1, Perizat Kanabekova 1 2, Temirlan Kainazarov 1 *
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1 Limited Liability Partnership "Symptom", Almaty, Kazakhstan
2 Department of Medicine, School of Medicine, Nazarbayev University, Astana, Kazakhstan
* Corresponding Author
J CLIN MED KAZ, Volume 20, Issue 6, pp. 26-35. https://doi.org/10.23950/jcmk/13947
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ABSTRACT

The practice of gathering a patient's medical history has been a cornerstone of healthcare for centuries, providing the foundation for accurate diagnoses and effective treatment plans. However, traditional face-to-face consultations have limitations, including incomplete histories due to time constraints and potential communication barriers. To address these challenges, pre-consultation history taking systems emerged as a transformative solution, leveraging technology to optimize data collection and patient engagement. This review article explores the evolution, benefits, limitations, and impact of pre-consultation history taking systems on modern healthcare practices. These systems enable patients to respond to questionnaires or surveys before their scheduled appointments, empowering them to provide comprehensive medical histories at their own pace. Consequently, healthcare providers gain deeper insights into patients' health status, previous medical conditions, family history, lifestyle choices, and medication history. The significance of pre-consultation history taking lies in its potential to improve the quality of healthcare services. By obtaining more detailed and accurate medical histories before appointments, healthcare providers can optimize consultation time, enabling them to focus on addressing specific concerns and making informed decisions. Furthermore, patient engagement is enhanced, fostering a sense of collaboration between patients and healthcare professionals. Despite the advantages, the article addresses certain limitations, such as the digital divide and data accuracy concerns. Ensuring accessibility for all patient populations and maintaining robust data security measures are essential considerations. However, as technology continues to advance, pre-consultation history taking holds the promise of transforming the healthcare landscape and improving patient outcomes.

CITATION

Zhakhina G, Tapinova K, Kanabekova P, Kainazarov T. Pre-consultation history taking systems and their impact on modern practices: Advantages and limitations. J CLIN MED KAZ. 2023;20(6):26-35. https://doi.org/10.23950/jcmk/13947

REFERENCES

  • Melms L, Schaefer JR, Jerrentrup A, Mueller T. A pilot study of patient satisfaction with a self-completed tablet-based digital questionnaire for collecting the patient's medical history in an emergency department. BMC Health Services Research. 2021;21(1):1-13. https://doi.org/10.1186/s12913-021-06748-y
  • Pappas Y, Anandan C, Liu J, Car J, Sheikh A, Majeed A, editors. Computer-assisted history-taking systems (CAHTS) in health care: benefits, risks and potential for further development. 2011;19(3):155-60. https://doi.org/10.14236/jhi.v19i3.808
  • Denecke K, Hochreutener SL, Pöpel A, May R. Self-anamnesis with a conversational user interface: concept and usability study. Methods of information in medicine. 2018;57(05/06):243-52. https://doi.org/10.1055/s-0038-1675822
  • Nishida A, Ogawa O. The Effect of a Pre-consultation Tablet-Based Questionnaire on Changes in Consultation Time for First-Visit Patients With Diabetes: A Single-Case Design Preliminary Study. Cureus. 2022;14(11). https://doi.org/10.7759/cureus.31624
  • Zakim D. Development and significance of automated history‐taking software for clinical medicine, clinical research and basic medical science. Journal of internal medicine. 2016;280(3):287-99. https://doi.org/10.1111/joim.12509
  • Hall MA, Zheng B, Dugan E, Camacho F, Kidd KE, Mishra A, et al. Measuring patients' trust in their primary care providers. Medical care research and review. 2002;59(3):293-318. https://doi.org/10.1177/1077558702059003004
  • Agaku IT, Adisa AO, Ayo-Yusuf OA, Connolly GN. Concern about security and privacy, and perceived control over collection and use of health information are related to withholding of health information from healthcare providers. Journal of the American Medical Informatics Association. 2014;21(2):374-8. https://doi.org/10.1136/amiajnl-2013-002079
  • Campos-Castillo C, Anthony DL. The double-edged sword of electronic health records: implications for patient disclosure. Journal of the American Medical Informatics Association. 2015;22(e1):e130-e40. https://doi.org/10.1136/amiajnl-2014-002804
  • Kassirer JP. Imperatives, expediency, and the new diagnosis. Diagnosis. 2014;1(1):11-2. https://doi.org/10.1515/dx-2013-0004
  • Moonen P-J, Mercelina L, Boer W, Fret T. Diagnostic error in the Emergency Department: follow up of patients with minor trauma in the outpatient clinic. Scandinavian journal of trauma, resuscitation and emergency medicine. 2017;25:1-7. https://doi.org/10.1186/s13049-017-0361-5
  • Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L, et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Annals of internal medicine. 2016;165(11):753-60. https://doi.org/10.7326/M16-0961
  • Slack WV. A history of computerized medical interviews. Buying Equipment and Programs for Home or Office: Springer. 1984; 138-45. https://doi.org/10.1007/978-1-4612-4708-1_22
  • Grossman JH, Barnett GO, McGuire MT, Swedlow DB. Evaluation of computer-acquired patient histories. JAMA. 1971;215(8):1286-91. https://doi.org/10.1001/jama.1971.03180210032006
  • Harada Y, Shimizu T. Impact of a commercial artificial intelligence-driven patient self-assessment solution on waiting times at general internal medicine outpatient departments: retrospective study. JMIR Medical Informatics. 2020;8(8):e21056. https://doi.org/10.2196/21056
  • Soekhai V, de Bekker-Grob EW, Ellis AR, Vass CM. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics. 2019;37:201-26. https://doi.org/10.1007/s40273-018-0734-2
  • Oliveri S, Lanzoni L, Petrocchi S, Janssens R, Schoefs E, Huys I, et al. Opportunities and challenges of web-based and remotely administered surveys for patient preference studies in a vulnerable population. Patient preference and adherence. 2021:2509-17. https://doi.org/10.2147/PPA.S327006
  • Kumar A, Joshi S, editors. Applications of AI in healthcare sector for enhancement of medical decision making and quality of service. 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand. 2022; 37-41. https://doi.org/10.1109/DASA54658.2022.9765041
  • Tang H, Huang H, Liu J, Zhu J, Gou F, Wu J, editors. AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma. 2022;10(11):2313. https://doi.org/10.3390/healthcare10112313
  • Gellert GA, Orzechowski PM, Price T, Kabat-Karabon A, Jaszczak J, Marcjasz N, et al. A multinational survey of patient utilization of and value conveyed through virtual symptom triage and healthcare referral. Frontiers in Public Health. 2023;10:5267. https://doi.org/10.3389/fpubh.2022.1047291
  • Locke S, Bashall A, Al-Adely S, Moore J, Wilson A, Kitchen GB. Natural language processing in medicine: a review. Trends in Anaesthesia and Critical Care. 2021;38:4-9. https://doi.org/10.1016/j.tacc.2021.02.007
  • Kolanu N, Brown AS, Beech A, Center J, White CP. OR29-02 natural language processing of radiology reports improves identification of patients with fracture. Journal of the Endocrine Society. 2020;4(Supplement_1):OR29-02. https://doi.org/10.1210/jendso/bvaa046.1619
  • Lin SY, Mahoney MR, Sinsky CA. Ten ways artificial intelligence will transform primary care. Journal of general internal medicine. 2019;34:1626-30. https://doi.org/10.1007/s11606-019-05035-1
  • Goyder CR, Jones CHD, Heneghan CJ, Thompson MJ. Missed opportunities for diagnosis: lessons learned from diagnostic errors in primary care. British Journal of General Practice. 2015;65(641):e838-e44. https://doi.org/10.3399/bjgp15X687889
  • Davenport S, Goldberg D, Millar T. How psychiatric disorders are missed during medical consultations. The Lancet. 1987;330(8556):439-41. https://doi.org/10.1016/S0140-6736(87)90970-6
  • Palermo TM, Valenzuela D, Stork PP. A randomized trial of electronic versus paper pain diaries in children: impact on compliance, accuracy, and acceptability. Pain. 2004;107(3):213-9. https://doi.org/10.1016/j.pain.2003.10.005
  • Semigran HL, Levine DM, Nundy S, Mehrotra A. Comparison of physician and computer diagnostic accuracy. JAMA internal medicine. 2016;176(12):1860-1. https://doi.org/10.1001/jamainternmed.2016.6001
  • Kawamura R, Harada Y, Sugimoto S, Nagase Y, Katsukura S, Shimizu T. Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study. JMIR Medical Informatics. 2022;10(1):e35225. https://doi.org/10.2196/35225
  • Zakim D, Brandberg H, El Amrani S, Hultgren A, Stathakarou N, Nifakos S, et al. Computerized history-taking improves data quality for clinical decision-making-Comparison of EHR and computer-acquired history data in patients with chest pain. Plos one. 2021;16(9):e0257677. https://doi.org/10.1371/journal.pone.0257677
  • Kneuertz PJ, Jagadesh N, Perkins A, Fitzgerald M, Moffatt-Bruce SD, Merritt RE, et al. Improving patient engagement, adherence, and satisfaction in lung cancer surgery with implementation of a mobile device platform for patient reported outcomes. Journal of thoracic disease. 2020;12(11):6883. https://doi.org/10.21037/jtd.2020.01.23
  • Jamal F, Zouaghi O, Leroux PY, Staat P, Garrier O, Sanchez I, et al. Can digital pre-consultation save medical time and improve outcome in cardiology? Archives of Cardiovascular Diseases Supplements. 2019;11(1):153-4. https://doi.org/10.1016/j.acvdsp.2018.10.341
  • Montazeri M, Multmeier J, Novorol C, Upadhyay S, Wicks P, Gilbert S. Optimization of patient flow in urgent care centers using a digital tool for recording patient symptoms and history: simulation study. JMIR Formative Research. 2021;5(5):e26402. https://doi.org/10.2196/26402
  • Müller F, Chandra S, Furaijat G, Kruse S, Waligorski A, Simmenroth A, et al. A Digital Communication Assistance Tool (DCAT) to obtain medical history from foreign-language patients: development and pilot testing in a primary health care center for refugees. International Journal of Environmental Research and Public Health. 2020;17(4):1368. https://doi.org/10.3390/ijerph17041368
  • Spinazze P, Aardoom J, Chavannes N, Kasteleyn M. The computer will see you now: overcoming barriers to adoption of computer-assisted history taking (CAHT) in primary care. Journal of Medical Internet Research. 2021;23(2):e19306. https://doi.org/10.2196/19306
  • Graffigna G, Barello S. Patient engagement in healthcare: pathways for effective medical decision making. Neuropsychol Trends. 2015;17:53-65. https://doi.org/10.7358/neur-2015-017-bare
  • Schwappach DLB. Engaging patients as vigilant partners in safety: a systematic review. Medical Care Research and Review. 2010;67(2):119-48. https://doi.org/10.1177/1077558709342254
  • Delbanco T, Walker J, Darer JD, Elmore JG, Feldman HJ, Leveille SG, et al. Open notes: doctors and patients signing on. American College of Physicians; 2010. p. 121-5.
  • Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association. 2016;23(5):899-908. https://doi.org/10.1093/jamia/ocv189
  • Nicholls M. Cardiologists and the burnout scenario. Oxford University Press. 2019. https://doi.org/10.1093/eurheartj/ehy788
  • Intermedica. Diagnostikare: Operational efficiency increased by 39% with Infermedica technologies. 2023.
  • BrightMD. Unlock efficiency for hybrid care with asynchronous telehealth. 2023.
  • Berg S. Physician burnout: Which medical specialties feel the most stress. AMA Physician Health. 2020.
  • Gholamzadeh M, Abtahi H, Ghazisaeeidi M. Applied techniques for putting pre-visit planning in clinical practice to empower patient-centered care in the pandemic era: a systematic review and framework suggestion. BMC Health Services Research. 2021;21(1):1-23. https://doi.org/10.1186/s12913-021-06456-7
  • Cowie MR, Blomster JI, Curtis LH, Duclaux S, Ford I, Fritz F, et al. Electronic health records to facilitate clinical research. Clinical Research in Cardiology. 2017;106:1-9. https://doi.org/10.1007/s00392-016-1025-6
  • Gusmanov A, Zhakhina G, Yerdessov S, Sakko Y, Mussina K, Alimbayev A, et al. Review of the research databases on population-based registries of Unified electronic Healthcare system of Kazakhstan (UNEHS): possibilities and limitations for epidemiological research and Real-World Evidence. International Journal of Medical Informatics. 2022:104950. https://doi.org/10.1016/j.ijmedinf.2022.104950
  • Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. Journal of big data. 2019;6(1):1-25. https://doi.org/10.1186/s40537-019-0217-0
  • Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. Journal of the American Medical Informatics Association. 2018;25(8):1080-8. https://doi.org/10.1093/jamia/ocy052
  • van Kessel R, Hrzic R, O'Nuallain E, Weir E, Wong BLH, Anderson M, et al. Digital health paradox: international policy perspectives to address increased health inequalities for people living with disabilities. Journal of medical Internet research. 2022;24(2):e33819. https://doi.org/10.2196/33819
  • Krouse HJ. COVID-19 and the widening gap in health inequity. Otolaryngology-Head and Neck Surgery. 2020;163(1):65-6. https://doi.org/10.1177/0194599820926463
  • Albrink K, Joos C, Schröder D, Müller F, Hummers E, Noack EM. Obtaining patients' medical history using a digital device prior to consultation in primary care: study protocol for a usability and validity study. BMC Medical Informatics and Decision Making. 2022;22(1):189. https://doi.org/10.1186/s12911-022-01928-0
  • Brandberg H, Sundberg CJ, Spaak J, Koch S, Zakim D, Kahan T. Use of Self-Reported Computerized Medical History Taking for Acute Chest Pain in the Emergency Department-the Clinical Expert Operating System Chest Pain Danderyd Study (CLEOS-CPDS): Prospective Cohort Study. Journal of Medical Internet Research. 2021;23(4):e25493. https://doi.org/10.2196/25493
  • Berdahl CT, Henreid AJ, Pevnick JM, Zheng K, Nuckols TK. Digital Tools designed to obtain the history of Present Illness from Patients: scoping review. Journal of Medical Internet Research. 2022;24(11):e36074. https://doi.org/10.2196/36074
  • Freise L, Neves AL, Flott K, Harrison P, Kelly J, Darzi A, et al. Assessment of patients' ability to review electronic health record information to identify potential errors: cross-sectional web-based survey. JMIR formative research. 2021;5(2):e19074. https://doi.org/10.2196/19074
  • Javaid M, Khan IH. Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. Journal of oral biology and craniofacial research. 2021;11(2):209-14. https://doi.org/10.1016/j.jobcr.2021.01.015
  • Almalawi A, Khan AI, Alsolami F, Abushark YB, Alfakeeh AS. Managing Security of Healthcare Data for a Modern Healthcare System. Sensors. 2023;23(7):3612. https://doi.org/10.3390/s23073612
  • Mukati N, Namdev N, Dilip R, Hemalatha N, Dhiman V, Sahu B. Healthcare assistance to COVID-19 patient using internet of things (IoT) enabled technologies. Materials today: proceedings. 2023;80:3777-81. https://doi.org/10.1016/j.matpr.2021.07.379
  • Becker S, Miron-Shatz T, Schumacher N, Krocza J, Diamantidis C, Albrecht U-V. mHealth 2.0: experiences, possibilities, and perspectives. JMIR mHealth and uHealth. 2014;2(2):e3328. https://doi.org/10.2196/mhealth.3328
  • Vo V, Auroy L, Sarradon-Eck A. Patients' perceptions of mHealth apps: meta-ethnographic review of qualitative studies. JMIR mHealth and uHealth. 2019;7(7):e13817. https://doi.org/10.2196/13817
  • Giunti G, Kool J, Romero OR, Zubiete ED. Exploring the specific needs of persons with multiple sclerosis for mHealth solutions for physical activity: mixed-methods study. JMIR mHealth and uHealth. 2018;6(2):e8996. https://doi.org/10.2196/mhealth.8996
  • Schnall R, Higgins T, Brown W, Carballo-Dieguez A, Bakken S. Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informatics. 2015;216:467.
  • Martinez PR. A qualitative study on patient perceptions towards mHealth technology among high risk, chronic disease patients: Harvard University; 2015.
  • Sebern MD, Sulemanjee N, Sebern MJ, Garnier‐Villarreal M, Whitlatch CJ. Does an intervention designed to improve self‐management, social support and awareness of palliative‐care address needs of persons with heart failure, family caregivers and clinicians? Journal of clinical nursing. 2018;27(3-4):e643-e57. https://doi.org/10.1111/jocn.14115
  • Watkins JOTA, Goudge J, Gómez-Olivé FX, Griffiths F. Mobile phone use among patients and health workers to enhance primary healthcare: A qualitative study in rural South Africa. Social Science & Medicine. 2018;198:139-47. https://doi.org/10.1016/j.socscimed.2018.01.011
  • Morrissey EC, Casey M, Glynn LG, Walsh JC, Molloy GJ. Smartphone apps for improving medication adherence in hypertension: patients' perspectives. Patient preference and adherence. 2018:813-22. https://doi.org/10.2147/PPA.S145647
  • Pedell S, Vetere F, Kulik L, Ozanne E, Gruner A, editors. Social isolation of older people: the role of domestic technologies. In Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction (OZCHI '10). Association for Computing Machinery, New York, NY, USA, 164-167. https://doi.org/10.1145/1952222.1952255