||5R37CA237452-03 Interpret this number
||Vanderbilt University Medical Center
||Prediction of Anti-Cancer Medication Discontinuation Via Patient Portal Messages and Structured Electronic Medical Records
Cancer is a leading cause of morbidity in the United States, with more than half a million deaths
estimated in 2019. Systemic cancer therapies are increasingly being designed as long-term oral
anti-cancer medications, given the increased convenience of a self-administered regimen. For
instance, patients with operable hormone-receptor-positive breast cancer are prescribed adjuvant
oral hormonal therapy, with an expectation that they continue their regimen for a minimum of 5
years to maximize the benefits. Although many oral therapies have proven effective in mitigating
cancer recurrence and mortality, discontinuation to these treatments are not uncommon. This is
a concern because medication discontinuation before the completion of a prescribed treatment
protocol leads to lower survival rates, increased risks of recurrence, and higher healthcare costs.
To improve treatment adherence and promise better healthcare delivery, it is essential for
healthcare providers to know when and why a cancer patient will discontinue their medications.
While there have been various investigations into regimen discontinuation, the focus of these
studies is either on knowledge discovery or intervention. While knowledge discovery focuses on
characterizing the potential factors that lead to medication discontinuation, intervention aims to
leverage discovered knowledge to design and test effective strategies to help patients adhere to
treatments. Because there are thousands of cancer patients, it is impossible for healthcare
providers to apply intervention to each of them. Limited medical resources need to be allocated
efficiently, such that patients with a higher risk of discontinuing medications will receive greater,
timely attention. Yet, the increasing integration of online communication and mobile computing
technologies into the healthcare domain are generating massive quantities of patient-generated
information. Thus, we propose to apply online patient-provider communications in a patient portal
to supplement traditional EMR data to better understand a cancer patient’s medical experience.
The central hypothesis of this project is that such communications together with structured EMRs
can be applied to learn and forecast oral anti-cancer medication discontinuation. The specific
aims of this project designed to test our central hypothesis are to 1) discover what has been
communicated in a patient portal; 2) infer how patient portal messages and structured EMRs are
associated with medication discontinuation; and 3) determine who are more likely to discontinue
medications. To the best of our knowledge, this is the first study to apply the messages in a patient
portal and structured EMRs to investigate medication discontinuation for cancer patients.
Mining Medication Use Patterns from Clinical Notes for Breast Cancer Patients Through a Two-Stage Topic Modeling Approach.
, Brown J.T.
, Barron M.
, Warner J.L.
, Yin Z.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2022; 2022, p. 303-312.
Predicting Hormonal Therapy Medication Discontinuation for Breast Cancer Patients using Structured Data in Electronic Medical Records.
, Warner J.L.
, Malin B.A.
, Yin Z.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2022; 2022, p. 359-368.