Literature DB >> 30380083

The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation.

Zhijun Yin1, Morgan Harrell2, Jeremy L Warner1,3, Qingxia Chen1,4, Daniel Fabbri1,5, Bradley A Malin1,4,5.   

Abstract

Objective: Online platforms have created a variety of opportunities for breast patients to discuss their hormonal therapy, a long-term adjuvant treatment to reduce the chance of breast cancer occurrence and mortality. The goal of this investigation is to ascertain the extent to which the messages breast cancer patients communicated through an online portal can indicate their potential for discontinuing hormonal therapy. Materials and
Methods: We studied the de-identified electronic medical records of 1106 breast cancer patients who were prescribed hormonal therapy at Vanderbilt University Medical Center over a 12-year period. We designed a data-driven approach to investigate patients' patterns of messaging with healthcare providers, the topics they communicated, and the extent to which these messaging behaviors associate with the likelihood that a patient will discontinue a prescribed 5-year regimen of therapy.
Results: The results indicates that messaging rate over time [hazard ratio (HR) = 1.373, P = 0.002], mentions of side effects (HR = 1.214, P = 0.006), and surgery-related topics (HR = 1.170, P = 0.034) were associated with increased risk of early medication discontinuation. In contrast, seeking professional suggestions (HR = 0.766, P = 0.002), expressing gratitude to healthcare providers (HR = 0.872, P = 0.044), and mentions of drugs used to treat side effects (HR = 0.807, P = 0.013) were associated with decreased risk of medication discontinuation. Discussion and
Conclusion: This investigation suggests that patient-generated content can inform the study of health-related behaviors. Given that approximately 50% of breast cancer patients do not complete a course of hormonal therapy as described, the identification of factors associated with medication discontinuation can facilitate real-time interventions to prevent early discontinuation.

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Year:  2018        PMID: 30380083     DOI: 10.1093/jamia/ocy118

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  9 in total

Review 1.  Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.

Authors:  Guergana K Savova; Ioana Danciu; Folami Alamudun; Timothy Miller; Chen Lin; Danielle S Bitterman; Georgia Tourassi; Jeremy L Warner
Journal:  Cancer Res       Date:  2019-08-08       Impact factor: 12.701

2.  Patient Messaging Content Associated with Initiating Hormonal Therapy after a Breast Cancer Diagnosis.

Authors:  Zhijun Yin; Jeremy L Warner; Qingxia Chen; Bradley A Malin
Journal:  AMIA Annu Symp Proc       Date:  2020-03-04

3.  Why Patient Portal Messages Indicate Risk of Readmission for Patients with Ischemic Heart Disease.

Authors:  Lina Sulieman; Zhijun Yin; Bradley A Malin
Journal:  AMIA Annu Symp Proc       Date:  2020-03-04

4.  Active neural networks to detect mentions of changes to medication treatment in social media.

Authors:  Davy Weissenbacher; Suyu Ge; Ari Klein; Karen O'Connor; Robert Gross; Sean Hennessy; Graciela Gonzalez-Hernandez
Journal:  J Am Med Inform Assoc       Date:  2021-11-25       Impact factor: 4.497

5.  Mining Medication Use Patterns from Clinical Notes for Breast Cancer Patients Through a Two-Stage Topic Modeling Approach.

Authors:  Kimberley Es Kondratieff; J Thomas Brown; Marily Barron; Jeremy L Warner; Zhijun Yin
Journal:  AMIA Annu Symp Proc       Date:  2022-05-23

6.  Predicting Hormonal Therapy Medication Discontinuation for Breast Cancer Patients using Structured Data in Electronic Medical Records.

Authors:  Congning Ni; Jeremy L Warner; Bradley A Malin; Zhijun Yin
Journal:  AMIA Annu Symp Proc       Date:  2022-05-23

Review 7.  The impact of medication side effects on adherence and persistence to hormone therapy in breast cancer survivors: A quantitative systematic review.

Authors:  Leanne Fleming; Sommer Agnew; Nicola Peddie; Megan Crawford; Diane Dixon; Iain MacPherson
Journal:  Breast       Date:  2022-05-14       Impact factor: 4.254

8.  Comparison of breast cancer surrogate subtyping using a closed-system RT-qPCR breast cancer assay and immunohistochemistry on 100 core needle biopsies with matching surgical specimens.

Authors:  Slavica Janeva; Toshima Z Parris; Salmir Nasic; Shahin De Lara; Karolina Larsson; Riccardo A Audisio; Roger Olofsson Bagge; Anikó Kovács
Journal:  BMC Cancer       Date:  2021-04-21       Impact factor: 4.430

9.  Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts.

Authors:  Julian C Hong; Andrew T Fairchild; Jarred P Tanksley; Manisha Palta; Jessica D Tenenbaum
Journal:  JAMIA Open       Date:  2020-12-05
  9 in total

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