Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients

doi:10.1093/jamia/ocaa263

. 2021 Mar 1;28(3):541-548.

doi: 10.1093/jamia/ocaa263.

Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients

Filipe R Lucini^{1

2}, Karla D Krewulak¹, Kirsten M Fiest^{1

3

4}, Sean M Bagshaw^{5

6}, Danny J Zuege^{1

6}, Joon Lee^{2

3

7}, Henry T Stelfox^{1

3}

Affiliations

¹ Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada.
² Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
³ Department of Community Health Sciences and O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
⁴ Department of Psychiatry & Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
⁵ Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, and Alberta Health Services, Edmonton, Alberta, Canada.
⁶ Critical Care Strategic Clinical Network, Alberta Health Services, Alberta, Canada.
⁷ Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

PMID: 33201981
PMCID: PMC7936522
DOI: 10.1093/jamia/ocaa263

Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients

Filipe R Lucini et al. J Am Med Inform Assoc. 2021.

. 2021 Mar 1;28(3):541-548.

doi: 10.1093/jamia/ocaa263.

Authors

Filipe R Lucini^{1

2}, Karla D Krewulak¹, Kirsten M Fiest^{1

3

4}, Sean M Bagshaw^{5

6}, Danny J Zuege^{1

6}, Joon Lee^{2

3

7}, Henry T Stelfox^{1

3}

Affiliations

¹ Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada.
² Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
³ Department of Community Health Sciences and O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
⁴ Department of Psychiatry & Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
⁵ Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, and Alberta Health Services, Edmonton, Alberta, Canada.
⁶ Critical Care Strategic Clinical Network, Alberta Health Services, Alberta, Canada.
⁷ Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

PMID: 33201981
PMCID: PMC7936522
DOI: 10.1093/jamia/ocaa263

Abstract

Objective: To apply natural language processing (NLP) techniques to identify individual events and modes of communication between healthcare professionals and families of critically ill patients from electronic medical records (EMR).

Materials and methods: Retrospective cohort study of 280 randomly selected adult patients admitted to 1 of 15 intensive care units (ICU) in Alberta, Canada from June 19, 2012 to June 11, 2018. Individual events and modes of communication were independently abstracted using NLP and manual chart review (reference standard). Preprocessing techniques and 2 NLP approaches (rule-based and machine learning) were evaluated using sensitivity, specificity, and area under the receiver operating characteristic curves (AUROC).

Results: Over 2700 combinations of NLP methods and hyperparameters were evaluated for each mode of communication using a holdout subset. The rule-based approach had the highest AUROC in 65 datasets compared to the machine learning approach in 21 datasets. Both approaches had similar performance in 17 datasets. The rule-based AUROC for the grouped categories of patient documented to have family or friends (0.972, 95% CI 0.934-1.000), visit by family/friend (0.882 95% CI 0.820-0.943) and phone call with family/friend (0.975, 95% CI: 0.952-0.998) were high.

Discussion: We report an automated method to quantify communication between healthcare professionals and family members of adult patients from free-text EMRs. A rule-based NLP approach had better overall operating characteristics than a machine learning approach.

Conclusion: NLP can automatically and accurately measure frequency and mode of documented family visitation and communication from unstructured free-text EMRs, to support patient- and family-centered care initiatives.

Keywords: communication; electronic medical records; family; intensive care units; natural language processing.

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References

1. Fix GM, VanDeusen Lukas C, Bolton RE, et al.Patient-centred care is a way of doing things: How healthcare employees conceptualize patient-centred care. Health Expect 2018; 21 (1): 300–7. doi: 10.1111/hex.12615 - PMC - PubMed
1. Fiest KM, McIntosh CJ, Demiantschuk D, et al.Translating evidence to patient care through caregivers: a systematic review of caregiver-mediated interventions. BMC Med 2018; 16 (1): 1–10. doi: 10.1186/s12916-018-1097-4 - PMC - PubMed
1. Davidson JE, Aslakson RA, Long AC, et al.Guidelines for family-centered care in the neonatal, pediatric, and adult ICU. Crit Care Med 2017; 45 (1): 103–28. - PubMed
1. Au SS, Roze Des Ordons AL, Amir Ali A, et al.Communication with patients’ families in the intensive care unit: a point prevalence study. J Crit Care 2019; 54: 235–8. - PubMed
1. Kreimeyer K, Foster M, Pandey A, et al.Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 2017; 73: 14–29. - PMC - PubMed

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[1] Fix GM, VanDeusen Lukas C, Bolton RE, et al.Patient-centred care is a way of doing things: How healthcare employees conceptualize patient-centred care. Health Expect 2018; 21 (1): 300–7. doi: 10.1111/hex.12615 - PMC - PubMed

[2] Fix GM, VanDeusen Lukas C, Bolton RE, et al.Patient-centred care is a way of doing things: How healthcare employees conceptualize patient-centred care. Health Expect 2018; 21 (1): 300–7. doi: 10.1111/hex.12615 - PMC - PubMed

[3] Fiest KM, McIntosh CJ, Demiantschuk D, et al.Translating evidence to patient care through caregivers: a systematic review of caregiver-mediated interventions. BMC Med 2018; 16 (1): 1–10. doi: 10.1186/s12916-018-1097-4 - PMC - PubMed

[4] Fiest KM, McIntosh CJ, Demiantschuk D, et al.Translating evidence to patient care through caregivers: a systematic review of caregiver-mediated interventions. BMC Med 2018; 16 (1): 1–10. doi: 10.1186/s12916-018-1097-4 - PMC - PubMed

[5] Davidson JE, Aslakson RA, Long AC, et al.Guidelines for family-centered care in the neonatal, pediatric, and adult ICU. Crit Care Med 2017; 45 (1): 103–28. - PubMed

[6] Davidson JE, Aslakson RA, Long AC, et al.Guidelines for family-centered care in the neonatal, pediatric, and adult ICU. Crit Care Med 2017; 45 (1): 103–28. - PubMed

[7] Au SS, Roze Des Ordons AL, Amir Ali A, et al.Communication with patients’ families in the intensive care unit: a point prevalence study. J Crit Care 2019; 54: 235–8. - PubMed

[8] Au SS, Roze Des Ordons AL, Amir Ali A, et al.Communication with patients’ families in the intensive care unit: a point prevalence study. J Crit Care 2019; 54: 235–8. - PubMed

[9] Kreimeyer K, Foster M, Pandey A, et al.Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 2017; 73: 14–29. - PMC - PubMed

[10] Kreimeyer K, Foster M, Pandey A, et al.Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 2017; 73: 14–29. - PMC - PubMed

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Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients

Affiliations

Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients

Authors

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Abstract

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