Primary care is the basic level of care, where patients start their treatment journey. Proactive management at primary care level improves health access and quality of care through proactive approach for specific patient sub-groups, improves integration of care at different levels of the health care system, and improves patient outcomes. The aim of the study was to describe family nurses’ perceptions of high-risk patients’ proactive management and identify further training needs; therefore, a descriptive and empirical two-stage study design was used. A convenience sample of 16 family nurses was recruited in the first stage. The study was conducted at five Estonian primary health centers. Data were collected by Modified Patient Assessment of Chronic Illness Care (MPACIC) online questionnaire between September and December 2020. Ethical approval was obtained. The nurses evaluated their proactive management of high-risk patients positively. Higher results were related to patient support, encouragement, involvement in everyday care, and individual goal setting questions. Lower results were related to referral to professionals, follow-up visits, and suggesting specific health related programs and events from which the patients could benefit. As a second stage of the study, a training program was developed based on the acquired results and training was conducted for 18 nurses at one primary health center, where care management has been used since 2017. The training program was found to be useful. Nurses need specific knowledge about the proactive management and care plan of high-risk patients, which can be ensured through systematic training. The subject of nurses’ perceptions could benefit from further study by qualitative methods.
1. WHO. Incidence, mortality and prevalence by cancer site, 2020.
https://gco.iarc.fr/today/data/factsheets/populations/990-eu-27-fact-sheets.pdf (accessed 2022-01-05).
2. WHO/IARC. Estimated number of new cases from 2020 to 2040. https://gco.iarc.fr/tomorrow/en/dataviz/bars?mode= population (accessed 2022-01-05).
3. National Institute for Health Development. PK10: Pahaloomuliste kasvajate esmasjuhud paikme, soo ja vanuse- rühma järgi (New cases of malignant tumors by site, sex and age group), 2020.
https://statistika.tai.ee/pxweb/et/Andmebaas/Andmebaas__02Haigestumus__04PahaloomulisedKasvajad/PK10.px/table/tableViewLayout2/
(accessed 2021-07-07).
4. National Institute for Health Development. SD21: Surmad põhjuse, soo ja vanuserühma järgi (Deaths by cause, sex and age group), 2021.
https://statistika.tai.ee/pxweb/et/Andme baas/Andmebaas__01Rahvastik__04Surmad/SD21.px/table/tableViewLayout2/ (accessed 2022-01-07).
5. Krzyszczyk, P., Acevedo, A., Davidoff, E. J., Timmins, L. M., Marrero-Berrios, I., Patel, M. et al. The growing role of precision and personalized medicine for cancer treatment. Technology, 2018, 6, 79–100.
https://doi.org/10.1142/S2339547818300020
6. Mathur, S. and Sutton, J. Personalized medicine could transform healthcare. Biomed. Rep., 2017, 7(1), 3–5.
https://doi.org/10.3892/br.2017.922
7. Kumar-Sinha, C. and Chinnaiyan, A. M. Precision oncology in the age of integrative genomics. Nat. Biotechnol., 2018, 36, 46–60.
https://doi.org/10.1038/nbt.4017
8. Schwartzberg, L., Kim, E. S., Liu, D. and Schrag, D. Precision oncology: Who, how, what, when, and when not? Am. Soc. Clin. Oncol. Educ. book., 2017, 37, 160–169.
https://doi.org/10.1200/EDBK_174176
9. Wolyniec, K., Sharp, J., Lazarakis, S., Mileshkin, L. and Schofield, P. Understanding and information needs of cancer patients regarding treatment-focused genomic testing: A systematic review. Psychooncology, 2020, 29(4), 632–638.
https://doi.org/10.1002/pon.5351
10. Nakayama, K., Osaka, W., Matsubara, N., Takeuchi, T., Toyoda, M., Noriyuki, O. et al. Shared decision making, physicians’ explanations, and treatment satisfaction: a cross-sectional survey of prostate cancer patients. BMC Med. Inform. Decis. Mak., 2020, 20(1), 334.
https://doi.org/10.1186/s12911-020-01355-z
11. European Commission. Europe’s Beating Cancer Plan.
https://health.ec.europa.eu/system/files/2022-02/eu_cancer-plan_en_0.pdf (accessed 2021-04-20).
12. Maes-Carballo, M., Muñoz-Núñez, I., Martín-Díaz, M., Mignini, L., Bueno-Cavanillas, A. and Khan, K. S. Shared decision making in breast cancer treatment guidelines: development of a quality assessment tool and a systematic review. Health Expect., 2020, 23(5), 1045–1064.
https://doi.org/10.1111/hex.13112
13. National Institute for Health Development. Vähitõrje tegevuskava 2021–2030 (Cancer Action Plan 2021–2030).
https://intra.tai.ee/images/Vähitõrjeplaan_avalik_20.12.20.pdf (accessed 2021-03-26).
14. Katz, S. J., Belkora, J. and Elwyn, G. Shared decision making for treatment of cancer: challenges and opportunities. J. Oncol. Pract., 2014, 10, 206.
https://doi.org/10.1200/JOP.2014.001434
15. Covvey, J. R., Kamal, K. M., Gorse, E. E., Mehta, Z., Dhumal, T., Heidari, E. et al. Barriers and facilitators to shared decision-making in oncology: a systematic review of the literature. Support. Care Cancer, 2019, 27(5), 1613–1637.
https://doi.org/10.1007/s00520-019-04675-7
16. Frerichs, W., Hahlweg, P., Müller, E., Adis, C. and Scholl, I. Shared decision-making in oncology – a qualitative analysis of healthcare providers’ views on current practice. PLoS One, 2016, 11(3), e0149789.
https://doi.org/10.1371/journal.pone.0149789
17. Spees, L. P., Roberts, M. C., Freedman, A. N., Butler, E. N., Klein, W. M. P., Das, I. P. et al. Involving patients and their families in deciding to use next generation sequencing: results from a nationally representative survey of U.S. oncologists. Patient Educ. Couns, 2021, 104(1), 33–39.
https://doi.org/10.1016/j.pec.2020.03.001
18. van Veenendaal, H., Voogdt-Pruis, H. R., Ubbink, D. T., van Weelw, E., Koco, L., Schuurman, M. et al. Evaluation of a multilevel implementation program for timeout and shared decision making in breast cancer care: a mixed methods study among 11 hospital teams. Patient Educ. Couns., 2022, 105(1), 114–127.
https://doi.org/10.1016/j.pec.2021.05.005
19. Cockerham, W. C. Medical Sociology On The Move: New Directions In Theory. Springer, Berlin, 2013.
https://doi.org/10.1007/978-94-007-6193-3
20. Pellegrini, I., Rapti, M., Extra, J.-M., Petri-Cal, A., Apostolidis, T., Ferrero, J.-M. et al. Tailored chemotherapy based on tumour gene expression analysis: breast cancer patients’ misinterpretations and positive attitudes. Eur. J. Cancer Care, 2012, 21(2), 242–250.
https://doi.org/10.1111/j.1365-2354.2011.01300.x
21. Keij, S. M., van Duijn-Bakker, N., Stiggelbout, A. M. and Pieterse, A. H. What makes a patient ready for shared decision making? A qualitative study. Patient Educ. Couns., 2021, 104(3), 571–577.
https://doi.org/10.1016/j.pec.2020.08.031
22. European Commission. Eurobarometer Qualitative Study on patient involvement in healthcare.
https://ec.europa.eu/eip/ageing/library/eurobarometer-qualitative-study-patient-invo lvement-healthcare_en.html
(accessed 2021-03-19).
23. Liang, R., Meiser, B., Smith, S., Kasparian, N. A., Lewis, C. R., Chin, M. et al. Advanced cancer patients’ attitudes towards, and experiences with, screening for somatic mutations in tumours: a qualitative study. Eur. J. Cancer Care, 2017, 26(6), e12600.
https://doi.org/10.1111/ecc.12600
24. Gray, S. W., Hicks-Courant, K., Lathan, C. S., Garraway, L., Park, E. R. and Weeks, J. C. Attitudes of patients with cancer about personalized medicine and somatic genetic testing. J. Oncol. Pract., 2012, 8(6), 329–335.
https://doi.org/10.1200/JOP.2012.000626
25. Gaston, C. M. and Mitchell, G. Information giving and decision-making in patients with advanced cancer: a systematic review. Soc. Sci. Med., 2005, 61(10), 2252–64.
https://doi.org/10.1016/j.socscimed.2005.04.015
26. Pritchard, D. E., Moeckel, F., Villa, M. S., Housman, L. T., McCarty, C. A. and McLeod, H. L. Strategies for integrating personalized medicine into healthcare practice. Per. Med., 2017, 14(2), 141–152.
https://doi.org/10.2217/pme-2016-0064
27. Kallaste, E., Järve, J., Sõmer, M., Lang, A. et al. Inimkeskse tervishoiu seiremetoodika väljatöötamine: lõpparuanne (Report of development of human health monitoring methodology). Sotsiaalministeerium, Tallinn, 2019 (in Estonian).
https://centar.ee/pdf/ee/2019_Inimkeskse_tervishoiu_seiremetoodika_valjatootamine.pdf (accessed 2021-05-15).
28. Ministry of Social Affairs. Eesti elanike hinnangud tervisele ja arstiabile (Estonian residents’ assessments of health and medical care), 2019 (in Estonian).
https://www.haigekassa.ee/sites/default/files/uuringud_aruanded/kuvandiuuring/Arstiabiuuringuaruanne2020.pdf
(accessed 2021-05-15).
29. Sandman, L. and Munthe, C. Shared decision making, paternalism and patient choice. Health Care Anal., 2010, 18(1), 60–84.
https://doi.org/10.1007/s10728-008-0108-6
30. Statistics Estonia. RV0222U: Population by sex, ethnic nationality and county, 1 January, administrative division as at 01.01.2018.
http://andmebaas.stat.ee/Index.aspx?DataSetCode=RV0222U&lang=en (accessed 2021-07-06).
31. Lang, T. A. and Altman, D. G. Basic statistical reporting for articles published in biomedical journals: the ‘statistical analyses and methods in the published literature’ or the SAMPL guidelines. Int. J. Nurs. Stud., 2015, 52(1), 5–9.
https://doi.org/10.1016/j.ijnurstu.2014.09.006
32. Yu, L., Zheng, F., Xiong, J. and Wu, X. Relationship of patient-centered communication and cancer risk information avoidance: A social cognitive perspective. Patient. Educ. Couns., 2021, 104(9), 2371–2377.
https://doi.org/10.1016/j.pec.2021.02.004
33. Amuta, A. O., Chen, X., Mkuu, R. The effect of cancer information seeking on perceptions of cancer risks, fatalism, and worry among a U.S. national sample. Am. J. Heal. Educ., 2017, 48, 366–373.
https://doi.org/10.1080/19325037.2017.1358119
34. Anker, A. E., Reinhart, A. M. and Feeley, T. H. Health information seeking: A review of measures and methods. Patient Educ. Couns., 2011, 82, 346–354.
https://doi.org/10.1016/j.pec.2010.12.008
35. Riigikogu. Personal Data Protection Act 2018.
https://www.riigiteataja.ee/en/eli/523012019001/consolide
36. Statistics Estonia. Income 2020.
https://www.stat.ee/en/find-statistics/statistics-theme/work-life/income (accessed 2021- 05-21).
37. National Institute for Health Development. ETU30: Haiguste esinemine soo ja vanuserühma järgi (Incidence of diseases by sex and age group).
https://statistika.tai.ee/pxweb/et/Andmebaas/Andmebaas__05Uuringud__01ETeU__03Haigused/ETU30.px/table/tableViewLayout2/
(accessed 2021-05-14).
38. Ministry of Social Affairs. Genome Project 2019.
https://www.sm.ee/en/news/genome-project-100000-samples-collected-2019-least-50000-more-people-can-join
(accessed 2021- 04-16).
39. Habicht, T., Reinap, M., Kasekamp, K., Sikkut, R., Aaben, L. and Van Ginneken, E. Estonia: Health system review. Health Systems in Transition, 2018, 20(1).
https://www.healthobservatory.eu (accessed 2021-04-15).
40. Rechel, B., Richardson, E. and Mckee, M. Trends in health systems in the former Soviet countries. Observatory Studies Series, 2014, 35.
https://www.euro.who.int/__data/assets/pdf_file/0019/261271/Trends-in-health-systems-in-the-former-Soviet-countries.pdf
(accessed 2021-04-16).
41. Statistics Estonia. IT20: Arvuti ja koduse interneti- ühendusega leibkonnad tüübi ja elukoha järgi (Households with computer and home internet connection by type of and place of residence) 2021.
https://andmed.stat.ee/et/stat/majandus__infotehnoloogia__infotehnoloogia-leibkonnas/IT20
42. Uibu, M. and Vihalemm, T. Tervisepüüdlused ühiskonna polariseerijana (Pursuits to health as polarisers of the society). In Eesti ühiskond kiirenevas ajas(Estonian Society In An Accelerating Time) (Vihalemm, P., Lauristin, M., Kalmus, V. and Vihalemm, T., eds). Tartu Ülikooli Kirjastus, Tartu, 343–356.
43. Lubi, K., Raal, A. and Taba, P. Ethnic identity in transition: the potential impact of ethnicity on chronic illness’ medication adherence in post-Soviet country. J. Racial Ethn. Health Disparities, 2022, 9, 1089–1095.
https://doi.org/10.1007/s40615-021-01048-x
44. Vihalemm, P. and Lauristin, M. Hinnangud muutustele ja maailmavaatelised hoiakud. In Eesti ühiskond kiirenevas ajas (Estonian Society In An Accelerating Time) (Vihalemm, P., Lauristin, M., Kalmus, V. and Vihalemm, T., eds). Tartu Ülikooli Kirjastus, Tartu, 139–153.
45. Smith, S. G., Pandit, A., Rush, S. R., Wolf, M. S. and Simon, C. J. The role of patient activation in preferences for shared decision making: results from a national survey of U.S. adults. J. Health Commun., 2016, 21(1), 67–75.
https://doi.org/10.1080/10810730.2015.1033115
46. Statistics Estonia. IT32: 16–74-aastased arvuti- ja inter- netikasutajad isikute rühma järgi (Computer and Internet users aged 16–74), 2020.
https://andmed.stat.ee/et/stat/majandus__infotehnoloogia__infotehnoloogia-leibkonnas/IT32/table/tableViewLayout1
(accessed 2021-07-07).