Healthc Inform Res.  2024 Oct;30(4):364-374. 10.4258/hir.2024.30.4.364.

Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan

• Kawasaki Y
• Nii M
• Nishioka E

• KMID: 2560539
• DOI: http://doi.org/10.4258/hir.2024.30.4.364

Abstract

Objectives
This study was performed to examine the content of decision-making support and patient responses, as documented in the nursing records of individuals with cancer. These patients had received outpatient treatment at hospitals that met government requirements for providing specialized cancer care.
Methods
Nursing records from the electronic medical record system (in the subjective, objective, assessment, and plan [SOAP] format), along with data from interviews, were extracted for patients receiving outpatient care at the Department of Internal Medicine and Palliative Care and the Department of Breast Oncology. Data analysis involved simple tabulation and text mining, utilizing KH Coder version 3.beta.07d.
Results
The study included 42 patients from palliative care internal medicine and 60 from breast oncology, with mean ages of 70.5 ± 12.2 and 55.8 ± 12.2 years, respectively. Decisions most frequently regarded palliative care unit admission (25 cases) and genetic testing (24 cases). The assessment category covered keywords including (1) “pain,” “treatment,” “future,” “recuperation,” and “home,” as terms related to palliative care and internal medicine, as well as (2) “treatment,” “relief,” and “genetics” as terms related to breast oncology. The plan category incorporated keywords such as (1) “treatment,” “relaxation,” and “visit” and (2) “explanation,” “confirmation,” and “conveyance.”
Conclusions
Nurses appear crucial in evaluating patients’ symptoms and treatment paths during the decision-making support process, helping them make informed choices about future treatments, care settings, and genetic testing. However, when patients cannot make a decision solely based on the information provided, clinicians must address complex psychological concepts such as disease progression and the potential genetic impact on their children. Further detailed observational studies of nurses’ responses to patients’ psychological reactions are warranted.

Keyword

Nurses, Palliative Care, Decision Making, Nursing Records, Oncology Nursing

Figure

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  Figure 1 Frequencies of decisions on various matters: (A) internal medicine palliative care department and (B) breast oncology department. PCU: palliative care unit, DNAR: do not attempt resuscitation, CPR: cardiopulmonary resuscitation, BSC: best supportive care (provision of symptomatic relief alone without active cancer treatment). “Second opinion” refers to the request of a doctor’s opinion at another medical institution in addition to that of the currently treating physician.

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  Figure 2 Nursing records on decision support within electronic medical records (internal medicine palliative care department): a co-occurrence network analysis. “Degree” represents grouping of similarities.

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  Figure 3 Nursing records on decision support within electronic medical records (internal medicine palliative care department): a multidimensional scaling analysis.

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  Figure 4 Nursing records on decision support within electronic medical records (breast oncology department): a co-occurrence network analysis. “Degree” represents grouping of similarities.

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  Figure 5 Nursing records on decision support within electronic medical records (breast oncology department): a multidimensional scaling analysis.

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  Figure 6 Changes in patients and families after decision support: (A) internal medicine palliative care department and (B) breast oncology department.

Reference

References

1. Ministry of Health, Labor and Welfare. Trends in the spread of electronic medical record systems, etc. [Internet]. Tokyo, Japan: Ministry of Health, Labor and Welfare;2023. [cited at 2024 May 9] Available from: https://www.mhlw.go.jp/content/10800000/000938782.pdf.

2. Zhao Y, Tsubota T. The current status of secondary use of claims, electronic medical records, and electronic health records in epidemiology in Japan: narrative literature review. JMIR Med Inform. 2023; 11:e39876. https://doi.org/10.2196/39876.

3. Hernandez MN, Voti L, Feldman JD, Tannenbaum SL, Scharber W, Mackinnon JA, et al. Cancer registry enrichment via linkage with hospital-based electronic medical records: a pilot investigation. J Registry Manag. 2013; 40(1):40–7.

4. Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013; 20(1):144–51. https://doi.org/10.1136/amiajnl-2011-000681.

5. Lewis AE, Weiskopf N, Abrams ZB, Foraker R, Lai AM, Payne PR, et al. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc. 2023; 30(10):1730–40. https://doi.org/10.1093/jamia/ocad120.

6. Kruse CS, Stein A, Thomas H, Kaur H. The use of electronic health records to support population health: a systematic review of the literature. J Med Syst. 2018; 42(11):214. https://doi.org/10.1007/s10916-018-1075-6.

7. Townsend JS, Jones MC, Jones MN, Waits AW, Konrad K, McCoy NM. A case study of early-onset colorectal cancer: using electronic health records to support public health surveillance on an emerging cancer control topic. J Registry Manag. 2021; 48(1):4–11.

8. Gensheimer MF, Narasimhan B, Henry AS, Wood DJ, Rubin DL. Accuracy of electronic medical record follow-up data for estimating the survival time of patients with cancer. JCO Clin Cancer Inform. 2022; 6:e2200019. https://doi.org/10.1200/CCI.22.00019.

9. Sollie A, Sijmons RH, Helsper C, Numans ME. Reusability of coded data in the primary care electronic medical record: a dynamic cohort study concerning cancer diagnoses. Int J Med Inform. 2017; 99:45–52. https://doi.org/10.1016/j.ijmedinf.2016.08.004.

10. Conderino S, Bendik S, Richards TB, Pulgarin C, Chan PY, Townsend J, et al. The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control. BMC Med Inform Decis Mak. 2022; 22(1):91. https://doi.org/10.1186/s12911-022-01831-8.

11. Benedum CM, Sondhi A, Fidyk E, Cohen AB, Nemeth S, Adamson B, et al. Replication of real-world evidence in oncology using electronic health record data extracted by machine learning. Cancers (Basel). 2023; 15:1853. https://doi.org/10.3390/cancers15061853.

12. Knevel R, Liao KP. From real-world electronic health record data to real-world results using artificial intelligence. Ann Rheum Dis. 2023; 82(3):306–11. https://doi.org/10.1136/ard-2022-222626.

13. Khosravi B, Rouzrokh P, Erickson BJ. Getting more out of large databases and EHRs with natural language processing and artificial intelligence: the future is here. J Bone Joint Surg Am. 2022; 104(Suppl 3):51–5. https://doi.org/10.2106/JBJS.22.00567.

14. Sim JA, Huang X, Horan MR, Stewart CM, Robison LL, Hudson MM, et al. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: a systematic review. Artif Intell Med. 2023; 146:102701. https://doi.org/10.1016/j.artmed.2023.102701.

15. Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, et al. Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review. Comput Biol Med. 2023; 155:106649. https://doi.org/10.1016/j.compbiomed.2023.106649.

16. Sim JA, Huang X, Horan MR, Baker JN, Huang IC. Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review. Expert Rev Pharmacoecon Outcomes Res. 2024; 24(4):467–75. https://doi.org/10.1080/14737167.2024.2322664.

17. Adamson B, Waskom M, Blarre A, Kelly J, Krismer K, Nemeth S, et al. Approach to machine learning for extraction of real-world data variables from electronic health records. Front Pharmacol. 2023; 14:1180962. https://doi.org/10.3389/fphar.2023.1180962.

18. Zurynski Y, Ellis LA, Tong HL, Laranjo L, Clay-Williams R, Testa L, et al. Implementation of electronic medical records in mental health settings: scoping review. JMIR Ment Health. 2021; 8(9):e30564. https://doi.org/10.2196/30564.

19. Schwarz J, Barkas A, Blease C, Collins L, Hagglund M, Markham S, et al. Sharing clinical notes and electronic health records with people affected by mental health conditions: scoping review. JMIR Ment Health. 2021; 8(12):e34170. https://doi.org/10.2196/34170.

20. Dutta R, Gkotsis G, Velupillai SU, Downs J, Roberts A, Stewart R, et al. Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records. Front Psychiatry. 2023; 14:1217649. https://doi.org/10.3389/fpsyt.2023.1217649.

21. Kariotis TC, Prictor M, Chang S, Gray K. Impact of electronic health records on information practices in mental health contexts: scoping review. J Med Internet Res. 2022; 24(5):e30405. https://doi.org/10.2196/30405.

22. Weir C. Through the narrative looking glass: commentary on “impact of electronic health records on information practices in mental health contexts: scoping review”. J Med Internet Res. 2022; 24(5):e38513. https://doi.org/10.2196/38513.

23. Lafata JE, Shin Y, Flocke SA, Hawley ST, Jones RM, Resnicow K, et al. Randomised trial to evaluate the effectiveness and impact of offering postvisit decision support and assistance in obtaining physician-recommended colorectal cancer screening: the e-assist: colon health study-a protocol study. BMJ Open. 2019; 9(1):e023986. https://doi.org/10.1136/bmjopen-2018-023986.

24. Han HS, Lee KE, Suh YJ, Jee HJ, Kim BJ, Kim HS, et al. Data collection framework for electronic medical record-based real-world data to evaluate the effectiveness and safety of cancer drugs: a nationwide real-world study of the Korean cancer study group. Ther Adv Med Oncol. 2022; 14:17588359221132628. https://doi.org/10.1177/17588359221132628.

25. Epstein RS, Weerasinghe RK, Parrish AS, Krenitsky J, Sanborn RE, Salimi T. Real-world burden of chemotherapy-induced myelosuppression in patients with small cell lung cancer: a retrospective analysis of electronic medical data from community cancer care providers. J Med Econ. 2022; 25(1):108–18. https://doi.org/10.1080/13696998.2021.2020570.

26. Sezgin E, Hussain SA, Rust S, Huang Y. Extracting medical information from free-text and unstructured patient-generated health data using natural language processing methods: feasibility study with real-world data. JMIR Form Res. 2023; 7:e43014. https://doi.org/10.2196/43014.

27. Patra BG, Sharma MM, Vekaria V, Adekkanattu P, Patterson OV, Glicksberg B, et al. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Inform Assoc. 2021; 28(12):2716–27. https://doi.org/10.1093/jamia/ocab170.

28. Elston Lafata J, Shires DA, Shin Y, Flocke S, Resnicow K, Johnson M, et al. Opportunities and challenges when using the electronic health record for practice-integrated patient-facing interventions: the e-assist colon health randomized trial. Med Decis Making. 2022; 42(8):985–98. https://doi.org/10.1177/0272989X221104094.